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This paper analyzes truck parking patterns in urban freight loading zones by jointly modeling the vehicle arrival rates and the parking durations. Three models were explored: 1) Count data (Negative Binomial) for vehicle arrivals, 2) Survival (Weibull) model for parking duration and 3) A joint model for arrivals and duration. The count data model estimates the parking demand i.e., the rate of truck arrival, while the survival model estimates the probability that a truck is parked for one more minute. The joint model is compared with separate models for predictability and performance. The dataset used in this research is obtained using a mobile phone parking application, at eight loading zones in the city Vic, Spain over an 18-month period from July 2018 to December 2019, comprised of vehicle parking durations, date, time of arrival and departure, professional activity, and vehicle type (weight). The parking activity data are complemented with built in environment variables of the loading zones, such as the number of establishments in a certain radius, the average walking distance to establishments, the presence of pedestrian pavement, the number of traffic lanes, among others. The joint model outperforms the models estimating the arrival rates and durations separately in goodness of fit and predictability. The model results showed that truck arrival rates vary significantly across days of the week, months, and arrival times. The parking durations are highly dependent on professional activity, vehicle type, and size. Tuesdays and Wednesdays have higher arrival rates compared to other days of a week (except Sundays). Among activities, the transport and parcels require longer parking durations. Among the vehicle types, trucks with gross weight larger than 3.5 tons park longer. This paper concludes by explaining the potential of these modeling approaches in improving urban freight operations, evaluation of various policy implications, limitations, and future research. [ABSTRACT FROM AUTHOR]

• HSR affects air traffic directly and indirectly via airfare adjustment. • Airfare plays different roles in competition, feeding and long-term effects. • HSR quality and pre-entry airline market structure affect airfare adjustment. • Feeding effect dominates competition effect, causing more airline CO2 emissions. • Airfare adjustment mitigates HSR-induced air traffic and airline CO2 emissions. Airfare reduction is proposed by a theoretical paper to be a possible source of observed air traffic increase in some markets where high-speed rail (HSR) enters and competes with airlines. This paper aims to empirically test whether and to what extent the air traffic impact is channeled by the adjustment in airfares. To understand the varying empirical results found in the literature, we examine heterogeneous airline responses in traffic and airfare in relation to HSR qualities measured by HSR-air travel time difference and pre-entry market structure of airline as well as decompose HSR impacts into competition, feeding and long-term effects. Using a panel dataset of Chinese air routes, we find that airfare adjustment plays crucial roles in channeling HSR's air traffic impact. Our estimation suggests that HSR introduced over 16.5 million additional passengers to the sampled air routes in our study period, generating 2.17 million tons of extra CO2 emissions from air flights. However, these numbers would increase to 32.2 million additional passengers and 3.4 million tons of extra CO2 emissions after removing the price adjustment. [ABSTRACT FROM AUTHOR]

• Quantify impacts of subscription bundles on travel behaviour and sustainability. • Verify MaaS potential to achieve societal and commercial outcomes. • Suggest a new commercial model for MaaS. • Quantify the net impact of MaaS on car-based use and shared-mode volume. • Perform an economic analysis of MaaS using transaction cost theory and market design. Mobility as a Service, or MaaS, is a relatively new business model that aims to disrupt the passenger transport industry by integrating existing mobility services into an intuitive smartphone app that allows everyday travellers to search, book, use, and pay for all their transport needs. In a fully integrated ecosystem, MaaS is envisaged to integrate not only travel information and payment, but also mobility services and societal goals to obtain the so-called four levels of MaaS integration. This paper empirically assesses the prospects of having a commercially viable and environmentally sustainable MaaS. Leveraging high-quality data collected by GPS-tracking technology, ticketing management systems, and survey questionnaires over the five-month in-field trial of MaaS in Sydney, this paper develops a discrete-count modelling system to quantify, for the first time, the impacts of MaaS on users' travel behaviour and extra volume/revenue for shared modes. Based on the quantitative evidence obtained, the paper suggests a new commercial model for MaaS and identifies the likely opportunities and challenges faced by MaaS integrators. [ABSTRACT FROM AUTHOR]

This paper investigates household task allocation decisions for non-work activity episodes by activity purpose and accompanying person arrangements. The paper utilizes activity-travel behavior information of all adult members (above 19 years) from a family to model the activity episodes aggregated at the household level. Such an approach gives a better perspective on the latent propensity of household members to engage in out-of-home activities. The nature (substitution or complementary) of the inter- and intra-group interaction between household and non-household members is examined using a multivariate ordered probit model estimated on activity-travel behavior survey data, consisting of 1335 households and 3639 adult individuals, collected in Bhubaneswar Municipal Region, India. The exploratory analysis sheds light on the preferences of household members for pursuing an activity episode (shopping, pure escort, other maintenance, physically active recreation, and physically passive recreation) either alone or jointly with immediate family members or non-household (extended family members or members from social circle) members. The correlation matrix consisting of 19 dimensions suggests significant inter-group substitution effects across the four accompaniment types for different non-work activities. The intra-group effects are complementary in the case of joint pursuits highlighting the likelihood of adult household members adhering to the same accompanying group. The model also suggests that the effect of individual characteristics, household socio-demographics, and seasonal and temporal characteristics are more impactful than vehicle ownership and built environment features. The study also presents the elasticity estimates of selected control variables and discusses their policy implications from a broad viewpoint. [ABSTRACT FROM AUTHOR]

• Temporal dynamics of middle-income road-fuel demand elasticities are analyzed. • Temporal heterogeneity is less than cross-sectional heterogeneity. • Average income elasticity between 1 and 0.8 and average price elasticity near −0.2 always. • No statistically significant evidence demand became saturated or consumers less price sensitive. This paper determines whether road-fuel (gasoline plus diesel) income and price elasticities have changed over time in middle-income countries. To do so, the paper considers a balanced panel of 26 countries that spans 1990–2019. Also, the paper employs two methods that fully allow for cross-sectional heterogeneity, but vary to the extent that they allow for temporal heterogeneity: rolling window, mean group regressions and mean observation OLS, which estimates coefficients for each cross-section and each time period. While the elasticities demonstrate some temporal heterogeneity, such variances are less pronounced than the corresponding country-level heterogeneity. At any point in time, for middle-income countries, the average road-fuel income elasticity is between 1 and 0.8, and the average road-fuel price elasticity is very near −0.2. Lastly, we find no strong evidence that road-fuel demand has become saturated or that efficiency improvements have made consumers less price sensitive in middle-income countries. [ABSTRACT FROM AUTHOR]

The increased usage of alternative transportation modes during public transportation disruptions has been well documented in the literature. While many papers discuss the resilience of the disrupted mode or measure changes in the usage of various alternative modes, little research has addressed the capacity flexibility and the price fluctuations of the alternative modes—two important components of the quality of an alternative. This paper documents the capacity and price changes of BlaBlaCar during the nationwide French railway strike in 2018. We collected more than 1 million trip offers from the BlaBlaCar's API from April to July 2018, covering 82 representative intercity routes in France. Our empirical analysis shows that, on an average strike day, the number of offered seats increased by approximately 6 %, while the number of booked seats rose by 33 %. Despite the spikes in demand, prices remained stable during the strike. We argue that the price recommendation mechanism helped maintain the price stability on the platform during the strike. The mechanism is in fact an effective enforcement of the policy laid down by the French government to uphold the cost-sharing principle on ridesharing platforms. This paper presents a case of voluntary compliance by a private company to put in place a price recommendation mechanism that in effect stabilizes prices and contributes to the resilience of the whole transportation system against abnormal market conditions. [ABSTRACT FROM AUTHOR]

During the year 2020, the COVID-19 pandemic affected mobility around the world, significantly reducing the number of trips by public transport. In this paper, we study its impact in five South American capitals (i.e., Bogotá, Buenos Aires, Lima, Quito and Santiago). A decline in public transport patronage could be very bad news for these cities in the long term, particularly if users change to less sustainable modes, such as cars or motorbikes. Notwithstanding, it could be even beneficial if users selected more sustainable modes, such as active transport (e.g., bicycles and walking). To better understand this phenomenon in the short term, we conducted surveys in these five cities looking for the main explanation for changes from public transport to active and private modes in terms of user perceptions, activity patterns and sociodemographic information. To forecast people's mode shifts in each city, we integrated both objective and subjective information collected in this study using a SEM-MIMIC model. We found five latent variables (i.e., COVID-19 impact, Entities response , Health risk , Life related activities comfort and Subjective well-being), two COVID-19 related attributes (i.e. , new cases and deaths), two trip attributes (i.e., cost savings and time), and six socio-demographic attributes (i.e. , age, civil status, household characteristics, income level , occupation and gender) influencing the shift from public transport to other modes. Furthermore, both the number of cases and the number of deaths caused by COVID-19 increased the probability of moving from public transport to other modes but, in general, we found a smaller probability of moving to active modes than to private modes. The paper proposes a novel way for understanding geographical and contextual similarities in the pandemic scenario for these metropolises from a transportation perspective. [ABSTRACT FROM AUTHOR]

• Non-drivers with physical disabilities (PD) more prefer to use autonomous vehicles (AV) than drivers. • Persons with PD who live in urban areas prefer to use AV over others. • Persons with PD's intention to use AV predominantly depends on their view of AV, not their mobility habits. • AV have excellent potential to improve access to transportation and ensure transportation equity. • One solution is introducing AV in the public transport system following accessibility, reliability, and safety principles. Improving access to transportation and ensuring transportation equity for persons with physical disabilities is one of the global challenges. Autonomous vehicles are pointed out as the potential solution to these problems. The views of persons with physical disabilities about autonomous vehicles differed sharply and substantially compared to non-disabled and other persons with disabilities. Therefore, this paper investigates differences in acceptance of autonomous vehicles among different groups of persons with physical disabilities. The driving status criterion was used because this fact significantly impacts the quality of life and social status. The paper's main contribution is to help create policies and practices related to autonomous vehicles following the real needs of persons with physical disabilities. The research was conducted in a country with vast problems of inclusion of persons with disabilities and transportation equity – Serbia. The Bayesian linear regression models were created with weakly informative prior distributions. We found that non-drivers with physical disabilities prefer to use autonomous vehicles more than drivers. The key issues of the successful introduction of autonomous vehicles are attitudes, accessibility, and trust. Autonomous vehicles present an excellent opportunity to improve mobility and transportation equity for non-drivers with physical disabilities. Transportation stakeholders, vehicle manufacturers, and representative associations of persons with disabilities should cooperate closely, respecting the concept „Nothing about us, without us.". One solution for resolving recognized challenges is introducing autonomous vehicles in the public transport system following accessibility, reliability, and safety principles. [ABSTRACT FROM AUTHOR]

• The process and preliminary analysis of the one-week virtual, physical, and app activities survey are presented. • Combining online and offline survey platforms, providing incentives, and maintaining close communication with respondents contribute to the survey's success. • Integrating information on virtual and physical activities into conventional time-use data provides a better understanding of daily activity participation. • App usage by senior workers or those who reside in Jakarta tends to create multiple effects, either creation or avoidance of travel. The increasing prevalence of information and communication technology (ICT), particularly through smartphones, has profoundly shaped individuals' daily schedules, making it an integral part of travel behaviour research. However, comprehensive data on daily virtual and physical activities as well as app usage behaviour remains limited. This paper describes the implementation of a survey to capture cross-dimensional weekly virtual-physical activities diary, including the details of travel, in-home, and out-of-home behaviour, extended smartphone app usage behaviour, and the psychological and perceived built environment factors affecting them. To our knowledge, this is the first attempt to expand the panel time-use survey to comprehensively capture daily smartphone app usage, the impact of such usage on travel, and their underlying behaviour (motivations and planned behaviour) in developing countries. Furthermore, it aims to demonstrate the possible usefulness of this dataset in assessing the impact of ICT on travel. The survey was deployed in Jakarta, Bandung, Denpasar, and Cianjur in Indonesia from May 2022 to January 2023. The protocol, mechanism, location, instrument, survey process, and preliminary data are described in the paper. Out of all respondents approached in all cities, 1,193 respondents over 18 years of age, or only 39 %, participated in the study. Respondents reported engaging in 23–32 activities daily and multitasking with average of 2–3 activities. The data collection process for this survey highlighted the importance of utilising both paper-and-pencil surveys and digital questionnaires, providing assistance and incentives, and maintaining open communication with respondents. This approach ensured successful data collection from individuals residing in different Indonesian cities, taking into account their diverse ICT qualities, education levels, and standards of living. Preliminary analysis shows that different combinations of travel and time use characteristics correlate with individuals' sociodemographic profile and residential location but not all perceived built environment conditions and personality traits. Integrating information on virtual and physical activities into conventional time-use data provides a better understanding of daily activity participation. The result also shows that the impact of app usage on travel varies across sociodemographic groups and cities. Apps usage by senior workers tends to create varied effects, either creation or avoidance of travel. Residents of Jakarta, a megapolitan area that provides more opportunities and economic intensity, tend to use multiple apps to avoid taking trips and generating new activities. The result also demonstrates that leisure app usage has multiple effects on travel and that the generation of new activities is associated with spontaneous app usage decisions. These preliminary results and extensive variables collected in this study highlight opportunities for future research on ICT and travel. [ABSTRACT FROM AUTHOR]

In this paper I present the concept of "ease of travel". Ease of travel (EoT) refers to how easily people can travel given their travel motivation, travel skills, travel options, and travel quality. EoT may differ by mode and influence people's travel behaviour, participation in out-of-home activities and experience of travel. EoT differs from the commonly used (perceived) accessibility concept, which mainly focuses on activity destinations and (perceptions towards) built environment characteristics. EoT, on the other hand, focuses on travel and highlights personal abilities and motivations for travel. It may provide valuable insights in people's travel behaviour intentions and can help in creating more sustainable travel patterns, but also in stimulating better access to out-of-home activities and generating satisfying travel, thereby improving people's subjective well-being. EoT may serve as a user-friendly and policy-relevant tool to assess people's ability to travel. In this paper, a conceptual framework is presented and measures of EoT are tested using a sample of 2,593 University students and staff. Results suggest that a subdivision of EoT in four elements is reliable. Finally, I also present avenues for further research. [ABSTRACT FROM AUTHOR]

In this paper, we assess the impact of a fare-free public transport policy for overnight guests on travel mode choice to a Swiss tourism destination. The policy directly targets domestic transport to and from a destination, the substantial contributor to the CO 2 emissions of overnight trips. Based on a survey sample, we identify the effect with the help of the random element that the information on the offer from a hotelier to the guest varies in day-to-day business. We estimate a shift from private cars to public transport due to the policy of, on average, 14.8 and 11.6 percentage points, depending on the application of propensity score matching and causal forest. This knowledge is relevant for policy-makers to design future public transport policies for tourists. Overall, our paper exemplifies how such an effect of natural experiments in the transport and tourism industry can be properly identified. [ABSTRACT FROM AUTHOR]

This paper explores the integration of urban aerial mobility (UAM) into the conventional transport network equilibrium problem, where travelers have the option to choose land-air amphibious vehicles for their daily commuting trips. The construction process of a land-air collaboration composite transportation network is first presented. Then, we further delve into the characterization of impedance function for various link types, using the classical queuing model that accounts for system capacity constraints. Subsequently, we develop a novel land-air collaboration network equilibrium (LAC-NE) model integrating the UAM concept, and it is translated into an equivalent mathematical programming problem, which admits solution existence and uniqueness. To efficiently address the model, based on the path-based gradient projection (GP) algorithm, an improved version with the path generation scheme is proposed. We conduct numerical experiments under diverse scenario settings to demonstrate the applicability of the LAC-NE model. These experiments examine the effects of UAM mode on network performance, revealing that the inclusion of land-air collaboration mode travelers would lead to distinct equilibrium states and network performance indicators. Furthermore, in comparison to the traditional network equilibrium model, the proposed model takes into account advanced low-altitude transportation (ALT), effectively alleviating urban traffic congestion. This paper offers valuable insights for the development of transportation policies aimed at addressing these emerging transportation mode challenges. [ABSTRACT FROM AUTHOR]

Many travel surveys used in transportation planning still collect information over a single weekday while mobility and activity patterns fluctuate from day to day. However, the emergence of new continuous data sources has led to the generation of large amounts of longitudinal data. Therefore, this paper aims to value these two data types to cross-analyze the temporal variability of aggregated travel behaviors. A weighting method based on large-scale single-day survey samples was first developed to extend the representativeness of the daily trip diaries available in the three most recent Origin-Destination household travel surveys of Montreal, Canada, to the entire data collection period (four months in the fall). This method was then validated using passive data streams (smart card and count data) for three modes: car, subway, and bicycle. The variations in the daily use of these modes measured independently by the surveys and passive data were compared over several months of the fall period and throughout an average week. Indicators were also proposed to evaluate whether the two data sources capture the same trend and seasonality, especially the weekly rhythms. The results show that it is possible to accurately infer day-to-day variability in travel behavior from a large-scale cross-sectional single-day survey. Furthermore, this paper demonstrates the potential for complementarity between traditional surveys and emerging data (at least over the survey period), as well as the possibility to combine them since there are large similarities in the aggregated travel patterns they allow monitoring. [ABSTRACT FROM AUTHOR]

• Optimal protection against sea level rise can decrease transportation equity. • The model system incorporates hydrodynamic and transportation simulations. • A case study is based on the Communities of Concern in the SF Bay Area. • Accessibility and mobility of disadvantaged communities are studied. Transportation equity is an essential aspect of urban transportation planning. With climate change becoming inevitable, coastal cities are considering the mitigation of the impact of sea level rise on infrastructure. Transportation equity and sea level rise adaptation are usually considered separately. However, research pointed out that these two challenges could have considerable overlap and interaction. The present paper discusses transportation equity issues resulting from the impact of sea level rise and associated protection strategies. A case study of the San Francisco Bay Area points out cases where transportation equity can be negatively impacted when the optimal protection strategy against sea level rise is implemented. An integrated hydrodynamic and transportation model system is used in the present paper to demonstrate several scenarios where the most efficient protection strategies for the whole region increase the inequity that exists between the disadvantaged communities and other communities. Nevertheless, this impact can be mitigated with a relatively small addition to the protection strategy. The paper suggests that transportation equity cannot be overlooked in planning climate adaptation, as even the protection plan that maximizes benefits for the region may negatively impact the most vulnerable communities. [ABSTRACT FROM AUTHOR]

• Post COVID-19, a third of Scottish residents expect to use public transport less. • A random parameter bivariate probit model was estimated to analyse survey data. • Sociodemographic, perceptual and behaviour factors affected future bus & train use. • Perceived risk of COVID-19 infection significantly influenced future travel choices. • Frequent bus users in urban areas were less likely to abandon public transport. This paper examines the determinants of changes in future public transport use in Scotland after the COVID-19 pandemic. An online questionnaire was distributed to 994 Scottish residents in order to identify travel habits, attitudes and preferences during the different phases of the COVID-19 outbreak and travel intentions after the pandemic. Quota constraints were enforced for age, gender and household income to ensure the sample was representative of the Scottish population. The respondents indicated that they anticipated they would make less use of buses and trains at the end of the pandemic. Over a third expect to use buses (36%) and trains (34%) less, whilst a quarter expect to drive their cars more. As part of the analysis, a random parameter bivariate probit model with heterogeneity in the means of random parameters was estimated to provide insights into the socio-demographic, behavioural and perceptual factors which might affect future public transport usage. The inclusion of random parameters allows for the potential effects of unobserved heterogeneity within the independent variables to be captured, whilst making allowances for heterogeneity in the means of the random parameters. The model estimation showed that several factors, including pre-lockdown travel choices, perceived risk of COVID-19 infection, household size and region significantly affected intended future use of public transport. In addition, several variables related to age, region, pre-lockdown travel choices and employment status resulted in random parameters. The current paper contributes to our understanding of the potential loss of demand for public transport and the consequences for future equitable and sustainable mobility. Our findings are highly relevant for transport policy when developing measures to strengthen the resilience of the public transport system during and after the pandemic. [ABSTRACT FROM AUTHOR]

To inform policies aimed at more sustainable travel behaviour, previous research has investigated the concept of multimodality. The notion underlying this line of research is that increasing the degree of multimodality will lead to less car dependence and therefore more sustainable travel behaviour. This paper investigates multimodality by inferring modality styles and revealing their response to exogenous variation in the form of the weather. The main idea of this paper is that travellers with a more multimodal modality style are more sensitive to exogenous variation, and that they are therefore more likely to resort to the use of the car when 'car-favouring' conditions present themselves. The results show that the effects of weather conditions on mode choices do indeed differ between three modality styles. The identified modality styles can be summarised as (1) bike + car; (2) car mostly and (3) multimodal. For the third class, which has the highest degree of multimodality, the use of the sustainable modes is more strongly affected by weather conditions when compared to the first, less multimodal, class. The least multimodal second class meanwhile is least affected by a change in weather conditions. More multimodal travellers thus seem to be more susceptible to exogenous variation, which might prevent the formation of sustainable travel habits or patterns. Based on these results, the claim that a higher degree of multimodality will lead to more sustainable behaviour and that policy makers should aim to realise a shift towards more multimodal modality styles needs to be nuanced. Policy makers should instead focus directly on increasing the attractiveness of sustainable travel modes, which will inadvertently lead to more multimodal modality styles. [ABSTRACT FROM AUTHOR]

Much recent work on transport and the economy has focused on 'Wider Economic Impacts' (WEIs) of infrastructure investment, the impacts other than time savings benefiting those actually using the transport network. Differential effects of transportation infrastructure by mode such as urban rail and road are relatively well known. However, impacts of other mode such as walking are scarce. This paper estimates wider economic impacts related to productivity from full rail, road and walking transport networks in Hong Kong in 2016. To the best of our knowledge, this is the first paper that makes use of complex network science indicators with spatial cognition-weighted accessibility combining full urban rail network, road network, and pedestrian network in capturing wider economic impacts. We use an instrumental variable approach to identify the causal effect of transport network centralities on productivity measured by gross value added. Our identification strategy largely relies on the exogenous variations from historical planned and existing transport networks. A first specification confirms the significance of urban rail and road. Specification with pedestrian network shows that pedestrian and rail networks can statistically significantly increase productivity in Hong Kong while roads play a less significant but still meaningful role. Our findings are robust to a variety of sensitivity tests such as using night-time light intensity and residential wage as alternative measures for productivity. The research suggests a key planning policy implication: place-based policies in a dense city require improvement in pedestrian and rail network structure that impacts local and global transport accessibility. [ABSTRACT FROM AUTHOR]

Coronavirus 2019 (COVID-19) and its variants are still spreading rapidly with deadly consequences and profound impacts on the global health and world economy. Without a suitable vaccine, mobility restriction has been the most effective method so far to prevent its spreading and avoid overwhelming the heath system of the affected country. The compartmental model SIR (or Susceptible, Infected, and Recovered) is the most popular mathematical model used to predict the course of the COVID-19 pandemic in order to plan the control actions and mobility restrictions against its spreading. A major limitation of this model in relation to modeling the spreading of COVID-19, and the mobility limitation strategy, is that the SIR model does not include mobility or take into account changes in mobility within its structure. This paper develops and tests a new hybrid SIR model; SIR-M which is integrated with an urban activity travel model to explore how it might improve the prediction of pandemic course and the testing of mobility limitation strategies in managing virus spread. The paper describes the enhanced methodology and tests a range of mobility limitation strategies on virus spread outcomes. Implications for policy and research futures are suggested. [ABSTRACT FROM AUTHOR]

This paper conducts a stated preference (SP) survey by integrating vehicle attributes, policy attributes, and psychological characteristics to explore the mechanisms of personal carbon trading (PCT) and tradable driving credit (TDC), as well as consumer preferences for different types of policy instruments. Using a hybrid choice model (HCM), the empirical results obtained from 1546 Chinese consumers reveal that PCT and TDC are more powerful than most existing policy instruments in encouraging consumers to adopt electric vehicles (EVs). Moreover, although psychological characteristics (i.e., environmental awareness, perceived environmental benefits, and subjective norms) play an important role in explaining consumers' EV adoption behaviour, PCT and TDC primarily alter consumers' EV adoption decisions through economic motivations rather than through psychological motivations. Most importantly, the implementation of PCT and TDC enables consumers to adopt EVs while having no substantial impact on their intention to purchase conventional fuel vehicles (CFVs), thereby laying the groundwork for PCT and TDC to function effectively. This paper contributes to the EV adoption literature by systematically investigating the combined effects of different factors, especially market-oriented policy instruments, on consumers' vehicle adoption decisions. The findings also provide new insights for policymakers to design novel policy instruments to promote EV adoption. [ABSTRACT FROM AUTHOR]

Customer satisfaction has always been a pivotal indicator of public transport service quality. However, most existing studies on public transport satisfaction focused on passenger perception without considering the actual traveling performance. Thus, whether passenger perception veritably reflects the actual performance remains to be investigated. This paper adopts the gradient boosting decision trees approach to reveal the relationship between actual on-board performance and passenger satisfaction driven by individual perception. The work utilizes a mixed dataset combining customer satisfaction survey (CSS) with actual traveling data collected in Shijiazhuang, China. Instead of a direct equivalence, the results indicate the complex non-linear relationship between passenger perception and actual on-board performance. These findings enable deriving strategies for administrators and operators to improve the public transport service quality and customer satisfaction in a more effective and targeted way. • The relationship of actual performance, perception and satisfaction is explored. • This paper adopts the gradient boosting decision trees approach. • A merged dataset composed of questionnaire data and objective data is utilized. • The equal assumption is proved to be not established under some circumstances. • A more effective and targeted service quality strategy is suggested. [ABSTRACT FROM AUTHOR]

• Demand responsive freight deliveries are optimized with TSP tour length approximations. • Headways and zone areas for flexible‐route delivery systems are jointly optimized and closed‐form solutions are obtained. • The problem is reformulated as a Lagrangian relaxation problem to constrain an integer fleetsize. • The single‐zone model presented in this paper can be the modular basis for a multi‐zone model with possible freight flows among all zones. • The sensitivities of results to important input factors are analyzed. Freight delivery planning is a critical area in transportation and logistics. This paper presents a planning-level model for jointly optimizing headways and zone sizes for flexible-route freight deliveries. The model analyzes deliveries for a one-to-many demand pattern from one terminal to random destinations in a zone, which are reached through approximated travelling salesman problem (TSP) tours. Then, the problem is reformulated as a Lagrangian relaxation problem to constrain the fleet size to an integer value. The proposed model and closed-form solutions provide direct insights into the relations affecting those decision variables. Results from the base case study and sensitivity analyses indicate how various factors affect optimal service frequencies, delivery zone sizes, and costs of freight deliveries. Before detailed vehicle routing problems (i.e., actual delivery scheduling and operations) are solved, the proposed model should be useful for planning freight delivery systems, regarding critical decisions about geographic coverage of services, delivery frequencies, vehicle capacities, and other system characteristics. The developed model can also be used for pickups with a many-to-one demand pattern and extended to serve many-to-many demand patterns in multiple delivery zones through transfers at a central terminal. [ABSTRACT FROM AUTHOR]

Considering the lack of adequate cycling infrastructure networks in many cities, decision makers must face the challenge of designing connected bicycle facility networks to ensure safe and comfortable access to urban opportunities for cyclists and the usability of infrastructure. This paper addresses the maximal covering bicycle network design problem (MCBNDP). MCBNDP involves making investment decisions to build a cycling network that is aimed at maximizing the coverage of cyclists, while maintaining a minimum total network cost at its minimum. The derived network is subject to a budget limit and accounts for the entire connectivity and directness as fundamental bicycle network design criteria. Cyclists who are located at a given origin are considered covered by the network if a connected path of dedicated cycling infrastructure links them to their desired destination, within a maximum travel distance. We propose a mixed-integer linear programming (MILP) formulation, including a two-phase solution approach to solving the MCBNDP. In addition, using a commercial solver, our MILP formulation allows exact solutions to be obtained for large-scale instances with reasonable computing times for these types of problems. This MILP formulation is employed to solve a real instance that is applied to a wide territory of analysis in Medellin city (Colombia). The findings of this paper will contribute to existing literature and support urban policymakers to better spatially allocate the resources and, consequently, maximize the impact of their investments on connected cycling infrastructure networks. Our findings indicate that access to opportunities for cyclists can be easily favored by making small improvements to the existing infrastructure to guarantee safe, direct, and comfortable cycling infrastructure. Because we specify a maximum travel distance rather than a shortest path limitation, our problem ensures that cyclists may have several possibilities for their routes, which may go along the shortest path or any other alternative, however, without exceeding the maximum travel distance. In this direction, our findings confirm that prioritizing coverage, while accounting for full network connectivity, will benefit more cyclists because of the flexible configuration of the new network, which also may ensure its usability. [ABSTRACT FROM AUTHOR]

• Significant growth in most teleactivities (remote online activities) due to COVID-19. • Telework, online learning, telehealth, and virtual meetings increased during COVID-19. • Neighborhood density associated with more frequent telework and virtual meetings with friends and family during COVID-19. • More local facilities associated with more frequent online learning during COVID-19. • Short and long-term implications for mobility are discussed. This paper presents new evidence on changes in a broad range of teleactivities due to the Coronavirus disease (COVID-19) pandemic and investigates how the built environment relates to these changes. The paper relies on survey and geospatial data from Oslo and its surrounding Viken region in Norway. Findings suggest that most teleactivities increased due to COVID-19. Telework, teleconferencing, online learning, telehealth, and virtual meetings with friends and family all increased during COVID-19 compared to the pre-COVID-19 period. The next step in the analysis examined relationships between built environment characteristics and teleactivities before and during COVID-19. Telework and virtual meetings increased to a greater extent in denser neighborhoods than in lower-density neighborhoods. A larger increase in online learning was associated with lower neighborhood density, lower accessibility to public transport, and more local facilities. Numerous local facilities were associated with more frequent telework and virtual meetings both before and during COVID-19. The substantial COVID-19-induced increase in teleactivities found in the study highlights the potential of information and communications technology (ICT) for replacing travel for various activities. [ABSTRACT FROM AUTHOR]

• Largest meta-analysis of price elasticities with 2023 observations from 204 studies. • Original meta-analysis of length of long run with 386 observations from 47 studies. • Price elasticities vary with a large number of important policy relevant variables. • Meta-analyses' results challenge some official price elasticity recommendations. • Price elasticities and length of long run can be forecast for range of situations. Price elasticities are widely used in transport planning, forecasting and appraisal. This paper presents the largest ever meta-analysis of price elasticities of travel demand based on 2023 elasticities drawn from 204 British studies published between 1968 and 2020. A large number of credible variations in elasticities emerge that add to the evidence base, challenge some official recommendations and are useful to practitioners. The paper also presents an original meta-analysis of the length of time until the long run is achieved, based on 386 observations obtained from 47 studies. This is an important complement to the price elasticity meta-analysis and it also challenges official recommendations. Whilst the research is based on British evidence, the insights of a methodological nature at the least are transferable to other contexts. [ABSTRACT FROM AUTHOR]

This paper investigates Spanish drivers' perceptions of the main barriers existing in Spain to the purchase of zero-emission vehicles (ZEVs). Following a comprehensive literature review in this field, this paper quantifies, by means of a survey conducted in Spain of 1474 Spanish drivers, the drivers' desired levels for each barrier to consider ZEVs in their next purchase decision to replace their current usually-used car. The analysis of these reported levels with latent class cluster models revealed the existence, in the sample, of groups of consumers with homogeneous preferences regarding the barriers. These groups differ in terms of individuals' characteristics, the car to be replaced, and journeys made with it. The most flexible groups comprise individuals with a significant knowledge of ZEVs, which underscores the importance of educational policies for the promotion of the use of ZEVs. The desired levels of the barriers for each group are confronted with the current status of the barriers for certain ZEVs. This comparison reveals that Fuel Cell Electric Vehicles (FCEVs) would have great potential if they received government support, because their only barriers are economic (purchase price and fuel availability). This paper also quantifies the effects that purchase incentives and infrastructure investment policies could have in terms of higher FCEV penetration rates. [ABSTRACT FROM AUTHOR]

Due to the positive externalities of public transit, fares have been heavily subsidized throughout the United States and the world. Policymakers have designed these subsidies as a potential mechanism to reduce local air pollution. This paper evaluates the effectiveness of free-fare public transit subsidies in reducing air pollution. Studying the "Zero Fare for Better Air" initiative, I find no significant decrease in ground-level ozone due to free-fare public transit in Colorado. This paper also demonstrates how the free-fare initiative failed to improve welfare even when incorporating all the positive externalities of transit ridership. [ABSTRACT FROM AUTHOR]

• Decisions regarding strategic planning and service operations for urban air mobility systems are determined. • A new metric for modelling spatial equity is introduced. • A bi-objective optimisation model is proposed. • A scenario-based robust optimisation framework is employed to capture demand uncertainty. • Some valuable policy recommendations are proposed. With the rapid development of novel vehicle technologies, electric vertical take-off and landing (eVTOL) vehicles are becoming a new transport servicing model to achieve better urban air mobility (UAM) systems. The UAM systems can efficiently utilise low-altitude airspace resources, providing a solution to alleviate congestion in urban ground traffic. This paper delves into an integrated optimisation problem aimed at addressing decision-making processes related to the strategic planning and service operation of UAM systems, considering both demand uncertainty and spatial equity. The problem encompasses various decision components, including parking stand numbers at vertiports and vertistops, eVTOL fleet sizing as well as eVTOL fleet allocation and operations. Additionally, we introduce a spatial equity metric and establish a bi-objective optimisation model to balance the trade-off between service profitability and spatial equity considerations. We transform the bi-objective optimisation model to a tractable single-objective formulation using ε-constraint approach and linearisation technique. In this paper, we employ a scenario-based robust optimisation framework that incorporates the interval robust method to capture the demand uncertainty, enhancing resilience against uncertain factors. We evaluate the model performance using a small-scale example and further validate the proposed model through a real-world case study. Numerical analysis results demonstrate that the scenario-based robust optimisation framework can ensure the robustness of decision-making against the effect of uncertain conditions. Furthermore, numerical experiments reveal a trade-off between profitability and spatial equity, potentially requiring a partial sacrifice of profit to attain a desired equity level. Finally, we propose valuable policy recommendations to guide the decision-making processes of UAM service providers. [ABSTRACT FROM AUTHOR]

School transport services are essential to the society. As more families drive their children to school, stakeholders such as the school operators and policy-makers should work together to provide child-friendly and sustainable services like school bus transport. Given that this is a multi-stakeholder problem, it is complex to balance the objectives, for example when it comes to deciding the operators and the school bus service strategies. Currently, there is rather limited knowledge on the relevant policy-making mechanism, and the impact of public transport services used as school transport. To address this, this paper proposes a novel evolutionary game-based approach to reflect the "game" nature of this school transport operation problem and simulate the policy-making procedure, where positive external benefits, as well as intangible school benefits from green and child friendly transport are included. Essentially, the proposed approach should lead to an "equilibrium point" in term of cost-benefits and decision-makings for the involved operators. In this paper, two game players, i.e. schools and governments, are considered. It is found that (1) if both players offer services, i.e. competing, the schools tend to be conservative and stop offering services; (2) if some critical decision-making parameters such as the profit thresholds can be altered, the schools and the government can jointly operate services at "equilibrium" and maintain it sustainably. "Altering" such profit thresholds are possible via policy interventions or service designs, such as service advertisement and quality supervision. [ABSTRACT FROM AUTHOR]

Service trips, a frequently overlooked segment of urban traffic, represent a disproportionately large share of the negative impacts associated with commercial activity. The reason is that although service trips are less frequent than freight trips, they often take longer and thus occupy a significant share of commercial parking. Demand management programs seeking to move service trips off-hours can potentially reduce congestion and emissions but they have been understudied. This paper describes research conducted to investigate receivers' willingness to participate in off-hour programs for planned service activity. The research reported in this paper analyzes results from a survey of 189 business establishments in New York City and the Capital Region in New York State using descriptive analyses of the data, and by estimating a discrete choice model to gain insight into how business characteristics influence willingness to participate in off-hour services. To assess the impacts of a hypothetical off-hour services program, the authors applied the discrete choice model, together with service trip attraction models, to quantify the hours of daytime parking that could be eliminated by the proposed program in certain ZIP Codes in New York City and the Albany area. The research conducted led to the identification of numerous policy implications that will help policymakers understand and maximize the potential benefits of implementing off-hour service programs. [ABSTRACT FROM AUTHOR]

• Average weekly WFH frequency may rise from 0.69 (pre-COVID) to 1.29 (post-COVID). • Theory of Planned Behaviour used to examine intention to increase WFH post-COVID. • Intention influenced by subjective norms and perceived behavioural control. • Job type; access to tech, materials; employer and family support key determinants. • Determinants differ based on pre-COVID WFH experience and work location. COVID related lockdowns have forced many workers around the world into work from home (WFH) arrangements. While the proportion of people who work from home has typically been very low (4.5% in Melbourne in 2016), it is worth considering how trends might change once the world emerges out of COVID lockdowns. This paper aims to examine (1) the long-term impacts of COVID on WFH and (2) the psycho-social factors which will impact workers' intention to increase WFH post-COVID. The paper uses the Theory of Planned Behaviour as a lens to examine these psycho-social determinants. The study uses the survey responses of 1,364 workers which were collected in June-August 2020 as part of a larger survey (n = 2,158), which aimed to capture a representative population of Greater Melbourne. Quasi-longitudinal investigation of self-reported WFH frequency revealed that WFH as a share of average weekday employment increased 310% during COVID lockdown. In the longer term, when COVID is no longer an issue, WFH can be expected to be 75% higher than pre-COVID levels. Structural Equation Modelling was applied to the Theory of Planned Behaviour to understand motivators for and barriers to increasing WFH post-COVID. Perceived Behavioural Control (e.g. job type, technology, access to materials) and Subjective Norms (e.g. employer and family support) will be crucial determinants of the intention to work from home post-COVID. Attitudes regarding WFH will only have a weak impact on future intention to work from home, a finding which contrasts with previous research. [ABSTRACT FROM AUTHOR]

Drawing on transit smart card data allied with local weather station records over a 12-month period, this paper takes Brisbane, Australia as the study context and examines the way in which weather impose influences on public transit passenger's travel behaviour. In terms of transit passenger's spatiotemporal origin–destination (OD) information, a new travel behaviour indicator called stickiness is developed to reflect passenger's travel similarity in using the transit service over a period. By applying a suite of regression models, it shows that weather's effects on transit passenger's travel behaviour vary by temporal period and passenger type. In general, weather is shown to exert a stronger effect on passenger's stickiness during midday off-peak hours in comparison with either AM peak or PM peak. Across all types of passengers, child passengers are found to be most tolerant to weather changes and even be stickier to their regular travel pattern during AM peak. The modelling results also reveal that when it relates to journey-to-work or journey-to-school, weather's effects on alighting location are much smaller than other three OD-related travel behaviour features (i.e., boarding time, boarding location, and alighting time). In contrast with the postponement during AM peak, transit passengers are prone to bring forward their trip back home during PM peak in response to poor weather conditions. Moreover, this paper verifies that weather parameters are not singly perceived by transit passengers but as an interrelated unity in shaping transit passenger's travel behaviour. By enriching the weather–travel behaviour scholarship, the empirical findings of this study are helpful to render a holistic understanding of the weather–travel behaviour relationship and have important implications for transit operators in building a more weather-resilient transit system. [ABSTRACT FROM AUTHOR]

• Waiting time models for feeder-trunk and demand responsive service are revisited. • New formulation is developed to estimate mean waiting time for unreliable services. • Waiting time of passengers at destination is overlooked in previous studies. • Most of the previous studies underestimate the mean waiting time of passengers. • Classic optimum headway formulations are updated using the proposed approach. Waiting cost is usually considered as the highest cost imposed to passengers in transit systems. Efficient planning and operations of transit systems require accurate estimation of passengers' waiting time. While the assumption of half the headway, as the mean waiting time experienced by transit users, has been extensively used in waiting time cost estimation, it is not always a realistic assumption considering heterogeneous passengers, service irregularities, and different types of transit services. In addition, many transit studies considered the waiting times of passengers only at the origin, while waiting times can also be incurred at transfer points and the destination, the latter for passengers with fixed arrival time at their destination. In this paper, we developed new mean waiting time formulations for different transit systems, including feeder-trunk service, Dial-a-Ride service, and single route with unreliable service. All possible combinations of types of passenger (planning and non-planning, with fixed and flexible arrival time at destination) and service types (schedule-based/frequency-based and high-frequency/low-frequency) are considered to capture the underlying dynamics of transit systems. The developed approach in this paper could be utilised in transit studies to better model these underlying dynamics and to achieve better designs and more efficient operations. [ABSTRACT FROM AUTHOR]

• Socio-economic parameters explain the choice of means of transport. • Moreover, we identified psychological setting of potential users of transport technologies. • This approach сan be useful for the transportation policy and for related processes. This paper studies the willingness among Russia's population to try out three new transport technologies: electric cars, car-sharing, and autonomous driving. The assumption is that these three offerings will in the near future appear as autonomously driving vehicles booked on a subscription basis. Next to socio-economic parameters such as age, gender, place of living or holding a driver's licence, we introduce three measures: values of self-expression, attitudes towards science and technology and attitudes towards novelties in general to explain the likelihood to try out these transport innovations. Thereby, this paper increases the understanding of the preconditions that lead to wide-spread acceptance of transport innovations. An analysis of the psychological set-up of the respondents allowed for the identification of a group of enthusiasts that are excited to try out these new transportation offerings. We argue that application of such an approach deepens the understanding of social mechanisms behind technology adoption and can be useful for the identification of social groups that support related processes. [ABSTRACT FROM AUTHOR]

This paper addresses the question, "Will post-pandemic travel behaviour, when the virus has gone, be different to pre-pandemic travel?". It adopts an online survey where respondents were asked to report changes in travel during the various stages of the pandemic and expectations of future travel after the virus has gone. The paper focusses on commuting including total commuting, work from home (WFH), employment, travel mode volume and share and timing of morning commute trips using reported behaviour during pandemic shutdowns; and expectations of commuting when the virus has gone. The paper provides evidence that travel behaviour post-pandemic might be different to pre-pandemic travel. It suggests that after the pandemic, public transport ridership, which declined steeply during the pandemic, will return but not to pre-pandemic levels. A post-pandemic reduction effect of around 20% in transit commuting is expected. This effect is supported using secondary evidence from a number of international cities. Results imply a mode shift from public transport use to car driving; this will be particularly large for CBD/downtown areas and is likely to result in peak period traffic congestion after the virus has gone. Work from home increased substantially during the pandemic; this will reduce after the pandemic as enforced WFH is replaced by voluntary WFH. Nevertheless, a sustained future ongoing increase in WFH above pre-pandemic levels is suggested, acting to reduce peak commuting by 6% and commuting to Melbourne CBD by 20%. However, reductions in commuting due to WFH do not offset mode shift from public transport to car driving resulting in a net increase in car use after the pandemic. Infection fear is a new top concern of public transport users since the pandemic. This fear has transitioned from 'fresh infection fear'; the initial concerns when the pandemic started to 'residual infection fear'; a long-term effect when the virus has gone. Implications of the findings for research and practice are discussed. [ABSTRACT FROM AUTHOR]

Research on travel satisfaction in terms of its determinants and in terms of its relationship with subjective well-being has not taken account of the effort in travelling. This paper explicitly considers the role of effort and its three constituent elements (physical, cognitive and affective) by analysing the way each interacts with travel satisfaction and subjective well-being. The context of the study is Greater Sydney, New South Wales, Australia where respondents answered an internet-based survey. The paper has two main objectives. First to explore the benefit or otherwise of including 'effort' in analyses of travel satisfaction: the analysis shows that including effort provides additional explanation. Second, to explore the bidirectional nature of the interaction between travel satisfaction and subjective well-being. The analysis suggests that the bottom-up approach to well-being whereby the link is from travel satisfaction to well-being is stronger. The paper concludes with some policy implications of how transport policy can reduce effort and thereby make sustainable travel more appealing. [ABSTRACT FROM AUTHOR]

• We conducted a state-of-the-art RP and SP survey and presented its methodology with a total of 5,143 respondents, which covers 52,731 observations. • The development of On-Demand Transport (ODT) is popular in many cities. Urban Air Mobility (UAM) will become a new alternative mode of transportation. • This paper presents the travel behavior of respondents in each mode of transportation. • We explored willingness to pay (WTP): VTTS, VTAT, elasticity, and the preference of all choice alternatives, including ODT and UAM using pooled SP and RP data sets. The research presented in this paper analyzed two data sets Revealed Preference (RP) and Stated Preference (SP), obtained with a new travel diary and mode choice survey. This survey, called Mobility Jakarta, combined both RP and SP parts and was conducted in the Greater Jakarta region. This is the first survey that collected responses from a substantial sample of the whole metropolitan area population. We estimated the discrete choice model pooling SP and RP data sets. We explored the Willingness To Pay, e.g., the Value of Travel Time Savings, and the elasticity for all mode choice alternatives, including On-Demand Transport and Urban Air Mobility. [ABSTRACT FROM AUTHOR]

With the rapid development of high-speed rail (HSR) systems in China, the evaluation of the accessibility of HSR has become an important issue, especially for daily accessibility, or daytime round-trip travel (one-way journeys taking within 4 h), as this form of travel has significantly redefined the concept of the business trips are. This paper presents an improved approach to measuring potential-based accessibility by adding train frequency as an important weight and distinguishing the travel time into different intervals to measure accessibility. With a case study of Shandong Province, China, the paper verifies that the improved approach is more suitable for evaluating the daily accessibility and more practical for regional and transport planning than the original approach. First, the method is more effective in analyzing the differences in accessibility between cities and revealing more detailed changes or improvements in accessibility resulting from HSR. Second, there is a stronger correlation between the accessibility of different time intervals and the local economic level (GDP). Third, the method is more in line with the actual conditions in explaining the improved accessibility and corresponding spatial structures of a region. As a result, the improved approach provides an improved ability to better characterize the impacts of accessibility on regional development and will play an important role in regional planning and transportation planning. Finally, the paper also notes that the proposed approach is primarily suited for the middle-or macrolevel accessibility studies. [ABSTRACT FROM AUTHOR]

[Display omitted] • This study extends C-TAM-TPB model by integrating shared parking-specific constructs. • Perceived network externality affects acceptance via perceived usefulness and attitude as mediators. • Perceived risks of shared parking do not necessarily negatively affect drivers' attitudes. • Perceived usefulness is the most important factor in shaping shared parking acceptance. Despite the increasing interest in shared parking, little attention has been devoted to its perceptions and acceptance among drivers. To fill such a gap, this paper develops a novel theoretical framework in which perceived network externality, cost risk, safety risk, and management pressure are incorporated into the Combined Technology Acceptance Model and Theory of Planned Behavior (C-TAM-TPB). This paper merely focuses on positive network externality. First, a mathematical model is proposed to verify some of the hypotheses. The analytical results show that both a driver's utility and the shared parking platform's equilibrium demand are increasing in the effect of network externality and decreasing in the effect of risks. Second, a structural equation modeling approach with the partial least squares algorithm is employed for parameter estimation and model assessment. We conducted a quantitative study with 321 usable responses. The empirical results show that, except for management pressure, all the other constructs measured in the study had a significant total effect on user attitude and use intention towards shared parking. The corresponding amount of variance in attitude and use intention explained by our research model was 68.4% and 72.6%, respectively. Our findings shall provide useful insights into the better implementation of shared parking applications from managerial, technological, and operational perspectives. [ABSTRACT FROM AUTHOR]

Unlike many third-party data sources, targeted marketing (TM) data constitute holistic datasets, with disaggregate variables – ranging from socioeconomic and demographic characteristics to attitudes, propensities, and behaviors – available for most individuals in the population. These qualities, along with ease of accessibility and relatively low acquisition costs, make TM data an attractive source for the supplementation of traditional transportation survey data, which are facing growing threats to quality. This paper develops a typology demonstrating ways in which TM data can aid in the design of transport studies, as well as in the augmentation of modeling efforts and policy scenarios, allowing for improved understanding and forecasting of travel-related attributes. However, challenges associated with integrating, validating, and understanding TM variables have resulted in only a few transportation studies that have used these data thus far. In this paper, we provide a transportation discipline-specific resource for TM data, informed by our integration of an extensive TM database with both the National Household Travel Survey (Georgia subset) and a statewide travel behavior survey conducted in Georgia on behalf of the Georgia Department of Transportation. Using the resultant datasets, we validate TM data by means of several approaches, and find that the TM dataset reports gender, age, tenure, race, marital status, and household size with match rates ranging from 70% to 90% relative to both transportation surveys. However, we also identify biases in favor of population segments that may have more longstanding financial/transactional records (e.g., males, homeowners, non-minorities, and older individuals), biases comparable but not identical to those of survey data. While this work suggests wide-ranging implications for the use of TM data in transportation, we caution that flexible and responsible approaches to using these data are critical for staying abreast of evolving privacy regulations that govern third-party data sources such as these. [ABSTRACT FROM AUTHOR]

Mobility-as-a-service (MaaS) is a concept that has gained popularity and interest in the last few years. A significant determinant of the successful integration of a MaaS scheme is demand from passengers and potential users. The current research explores demand and willingness-to-pay for MaaS, focusing on different MaaS plans and offered services. An online survey in Greater Manchester, United Kingdom is utilized in order to explore and model such preferences and choices. Data from the survey includes socio-demographic data, current travel patterns, and a menu-based, stated preference experiment where the users were able to design their ideal MaaS plan. We model user choices by estimating a multinomial logit model, calculating also willingness-to-pay (WTP) for different levels of offered services in the MaaS package (public transport, car-sharing, bike-sharing and taxi). Results of the paper shed light into demand and quantitative analysis regarding MaaS, which the current literature lacks and outlines a price range for mobility services which is close to relevant literature in other locations for public transport and car-sharing and provides WTP estimates for taxi and bike-sharing. Results from this paper are useful both to potential MaaS operators and mobility service providers who would consider joining such initiatives; as we highlight user preferences and willingness-to-pay and to policymakers; outlining the overall preference for MaaS and the effect of socio-demographic and travel habits on MaaS plan choices. [ABSTRACT FROM AUTHOR]

Bus bunching is of particular concern and undesirable for both operators and passengers in high-frequency transit. In contrast to existing control methods, this paper proposes a novel control policy, namely, providing real-time bus crowding information (BCI) for passengers. It is believed that passengers would spontaneously distribute more evenly among buses and help to prevent bus bunching with the provision of BCI accompanying arrival time information due to the following mechanism. A proportion of passengers would be likely to wait a few more minutes for the next bus when the current bus is crowded and the next bus is more comfortable, and the boarding times of these passengers would make the next bus dwell longer and increase its headway from the previous bus. We formulate bus motion models incorporating passenger boarding choice under BCI to realize the policy in simulation experiments. The results demonstrate that the policy can reduce operation instability by approximately 20% in terms of bus headway and single-trip time. In addition, this policy can significantly reduce the in-vehicle crowdedness experienced by passengers by up to 25% at the cost of small increases in the mean journey time in some cases. The simulation experiments on a holding-controlled route also indicate that the proposed policy is able to coordinate with holding well. A sensitivity analysis further confirms that the policy's performance is robust even if the passengers have low inclinations to choose the next bus. The policy of providing BCI in this paper is especially effective for bus routes with high passenger demand and may have great application potential in practice. [ABSTRACT FROM AUTHOR]

This paper summarizes the research conducted by the authors to investigate time-dependent patterns in Freight Trip Generation (FTG). As part of the research, the authors estimated econometric FTG models as a function of establishment attributes and temporal variables, using multi-year establishment-level data collected during the period 2005–2014. These unique data—encompassing the period of economic turmoil around the 2008 collapse of Wall Street, and the period of recovery that followed—were collected in New York City from commercial establishments in various industry sectors. The comprehensive modeling effort undertaken considered four different functional forms, two combinations of establishment characteristics, two different metrics of time, and eleven different industry sectors defined by two-digits North-America Industry Classification System (NAICS). The resulting models were analyzed to assess the extent and magnitude of the time-dependent effects on FTG for the industry sectors in the sample. In its final sections, the paper discusses the implications for transportation planning and policy and summarizes the chief findings of the effort. [ABSTRACT FROM AUTHOR]

Transportation agencies routinely make repair decisions in numerous contexts which include the timing of different repair treatments over an extended period. The timing policy may be characterized either by a standalone manner that maximizes the outcome or utility of individual treatments at a given time, or by a holistic manner that maximizes the utility of all treatments together within a given horizon period. This study begins with a hypothesis that the effect of the sum of treatment prescriptions may be different from the sum of the effect of individual treatment prescriptions. The paper discusses separate practical applications where each of these two situations is pertinent. The study is based on the use of surface roughness as the indicator of pavement performance, and therefore, the framework includes the development of treatment-specific treatment performance jump and performance loss models in terms of roughness. The framework also includes identification of the optimal roughness thresholds for each pavement treatment as a standalone activity, followed by a determination of the thresholds for each treatment from a holistic standpoint. The latter involves the establishment of an optimal repair schedule over the pavement life cycle and recording the resulting roughness thresholds at which each treatment is recommended for application within the optimal schedule. Then the paper presents a case study to demonstrate application of the framework, and to compare the optimal-schedule thresholds versus the stand-alone thresholds. The case study involved fourteen treatment types, two surface material types (rigid and flexible) and three road functional classes. The results suggest that in this context of highway management, the optimal triggers associated with individual standalone repair treatments are not necessarily the same (and yield inferior outcomes) compared to the application thresholds of those treatments when they are considered within an optimized life-cycle schedule. The results can provide guidance to agencies as they examine or update their current roughness thresholds for each standard treatment and their long-term condition-based or time-based repair schedules. The paper's framework and results can also help agencies assess the consequences of deviations from a repair timing policy (delayed or hastened applications) due to lack of funds and other causes. [ABSTRACT FROM AUTHOR]