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Improving public transport accessibility (PTA) has been considered as an effective measure for promoting sustainable urban development. Based on the grid-level data in Nanjing, China, this paper explores the spatially heterogeneous effects of PTA on road traffic CO 2 emissions using a geographically weighted random forest (GWRF) model. A simulation-based counterfactual analysis framework is further proposed to predict the intervention effects of improving PTA. Two kinds of practical interventions, adding facilities and increasing service frequency, are considered in our counterfactual prediction. The effects of improving PTA across different areas are predicted and compared. Our results indicate that the GWRF model with a properly tuned bandwidth outperforms the conventional random forest model. The results of counterfactual analysis show that improving PTA could achieve greater environmental benefits in suburb areas. With the process of urbanization in Nanjing, the population and economy has grown rapidly in suburb areas. Therefore, it is reasonable to improve public transport services in these areas. Based on our findings, PTA has potential to make a significant contribution to sustainable development of urban transportation. Moreover, our findings with respect to heterogeneous effects likely improve the efficiency of local transport policies that target such areas, helping achieve greater environmental benefits. [ABSTRACT FROM AUTHOR]

Distinguishing commuters from non-commuters is important for transportation planning and traffic demand management. A framework to identify commuters using public transit is proposed based on a spatial-temporal clustering algorithm. The framework extracts commute trips by mining spatial-temporal travel patterns depending on whether the travelers' trip chains are complete. The commuting features in terms of travel frequency, time regularity, and spatial regularity are utilized to compare the difference in travel behavior between commuters and non-commuters. The framework is applied to trip data collected from an incentive program implemented by the Metropolitan Transport Commission of Korea. The data records spatial and temporal information on travelers' boarding and alighting points, as well as actual origins and destinations. One trip record constitutes a complete trip chain for travelers who joined the incentive program. The results show that commuters exhibit a higher travel frequency and more regular spatial-temporal travel patterns than non-commuters. It is found that not all commuters travel during morning and evening peaks; some commuters leave work after evening peak. The proposed identification framework based on the spatial-temporal clustering approach is capable of exploiting the inherent spatial-temporal travel patterns of travelers to achieve a reliable identification of commuters. [ABSTRACT FROM AUTHOR]

In this study, we aimed to investigate the influence of the built environment (BE) on commuter mode choice using machine learning models in a dense megacity context. We collected 10,150 home-based commuting trips data from Dhaka, Bangladesh. We then utilized three machine learning classifiers to determine the most accurate prediction model for predicting the mode of transportation chosen for commuting in Dhaka. Based on the predictive performance of the classifiers, we identified that the Random Forest (RF) algorithm performed the best. Using the RF model, this study also explored the relative importance of BE factors in predicting commute mode choice, identified nonlinear relationships between the BE factors and mode choice, and examined the interaction effects of these factors on mode selection. Our results reveal that, compared to socio-demographic factors, the BE substantially influence commuter travel behavior. The BE characteristics have a specific nonlinear threshold limit at which they can have a notable impact on lowering private car use, and private car use does not display a constant return of scale with BE. Their interaction effects illustrate the potential optimal combination of BE interventions to lower private car use for commuting. These findings hold substantial implications for urban environmental policy, emphasizing the need for transit-oriented development, travel demand management, and integrated land-use transportation planning to foster low-carbon transportation systems in cities like Dhaka. • Assess impacts of urban built environment (BE) on transport mode use. • Nonlinear effects of the BE on transport mode selection at home and workplace. • Random forest algorithm performed better in describing mode choice in Dhaka. • The BE is a major contributor to explaining transport mode use. • Urban and transport policies should consider nonlinear & interaction effects of BE. [ABSTRACT FROM AUTHOR]

We propose the Individual Experienced Utility-Based Synthesis (INEXUS) accessibility metric, which is developed to leverage an open-source agent-based regional transportation model. We include two specifications: the Potential INEXUS, which relates to an individual's potential set of mode alternatives and the Realized INEXUS, which reflects the optimal mode chosen by the agent. One advantage of using an agent-based approach is that it enables us to estimate individual agent-level behavior and travel needs. This addresses a commonly identified limitation of many existing accessibility metrics, which exhibit insensitivity to the heterogeneity of transportation preferences, opportunities, and constraints across subpopulations. While many system-level outcomes of interest may inform transportation planning, arguably an equally important consideration is that the system provides adequate and equitable access to goods and services for the broad spectrum of those traveling along its network. In many cases, average results do not reflect the experience of a majority – or even a significant – portion of the population. We apply our methods in a case study of alternative ridehail price scenarios to demonstrate the value of INEXUS distributions in evaluating differences in accessibility within and between population groups. [ABSTRACT FROM AUTHOR]

Air travelers originating from the catchment areas of airports – typically defined by administrative or geographic boundaries – will sometimes "leak" across these boundaries to farther, often larger airports that offer more attractive services and options. To investigate the attractiveness of different airports based on their proximity and geographic position relative to one another, we assess the airport choices of air passengers across a large region of the US Midwest that spans multiple administrative boundaries and airports. We combine a dataset of air tickets purchased by these passengers with other publicly available datasets, and apply mixed logit airport choice models based on air service attributes and airport access distance. We take multiple subsets of the total air tickets dataset using Halton sampling in order to achieve model estimation convergence. Model estimates are compared between data subsets, and consistently lead to the following key results. First, changes in air service attributes on short- and medium-length routes will have a more uniform influence on air passengers' airport choices, compared with long routes. Second, comparable proportional changes in air service attributes lead to greater changes in the catchment areas and market shares of small and medium airports, compared with large hubs. Within these service characteristics, airfare is found to impact passengers' airport preferences more than flight frequency. Finally, the catchment areas and market shares of small- and medium-size airports are influenced by the proximity and geographic location of neighboring airports; this differs from the catchments of large airports, which strongly extend into multiple jurisdictional boundaries in all directions despite the presence of surrounding airports. The study results provide further insight into the influence of air service attributes on long-distance airport choices over large geographic regions served by a diverse set of airports, and the resulting implications on multimodal transportation planning within and across such regions. [ABSTRACT FROM AUTHOR]

Travel behaviours of today's young adults may be considered environmentally sustainable. However, the risk of transportation-related social exclusion and consequently socially unsustainable travel behaviour among young adults, which may relate to their sustainable travel choices and lower car-reliance, remains less explored. Focusing on 4178 Canadian post-secondary students within the Greater Toronto and Hamilton Area, we examined four measures of activity participation for each student as indicators of social exclusion which include the number of daily activities, area covered per activity, distance travelled per activity, and time spent travelling per activity. We explored the association between students' activity participation patterns and their transportation mode choice patterns. A latent class analysis of short- and long-term travel choices revealed five transportation mode choice patterns, namely- Automobile Dependents, Transit Dependents, Cyclists, Pedestrians , and Multimodals. Results from Poisson and multiple linear regression models suggest that transportation mode choice patterns are significantly associated with how students participate in activities. Transit Dependents are more likely to face social exclusion due to limited activity participation. Students who reside in dense areas with better street connectivity demonstrate socially sustainable travel patterns. Future transportation planning and policy should be focused on addressing the travel needs of young adults with limited transportation opportunities and inefficient public transit. [ABSTRACT FROM AUTHOR]

One of the first and most critical steps involved in transportation studies is to determine the traffic zoning scheme. A traffic analysis zone (TAZ) is the main unit of the transportation analysis. In most transportation planning studies, the focus is only on the modeling process, while ignoring aspects such as TAZ definition, configuration, and design. Although the definition of TAZs is one of the key stages in transportation planning studies, a proper approach for the definition is still one of the unsolved issues. In this research, a TAZ with regular geometric shapes, called regular traffic analysis zones (RTAZs), is presented as a new approach to traffic zoning. On the other hand, the accuracy of calculating trip production, as the first step of well-known 4-step transportation demand modeling system, is of high necessity, because this step determines the total number of trips. In the current study, the effects of RTAZs on the results of trip production models (TPMs) for the city of Khoy in Iran were investigated. The results indicated that TPMs based on regular zoning system acted almost similar to traditional zoning system in estimating produced trips and, in some cases, even presented relatively better estimations. Regarding the difficulties encountered in the topic of the traditional traffic zoning system, the present work showed that RTAZs facilitated the modeling process and led to time and cost savings. [ABSTRACT FROM AUTHOR]

Transportation disruptions offer opportunities to study how people adapt to using new modes of transportation and have important implications for transportation policy and planning. Bikeshare has emerged as a new popular mode of transportation in recent years as it offers a fast, easy, and reliable way to travel short distances, and for its convenience as a first- and last-mile mode to complement transit. It also offers many social, environmental, and health-related benefits and has the potential to promote low-carbon mobility. This study examines changes in bikeshare ridership due to rail transit closures in the Washington, D.C. area and investigates how promoting bikeshare systems in large metropolitan areas could be beneficial in cases of transit disruptions – regardless of the type, cause, and duration. We use disaggregate trip history data to analyze the impact of three different transit closures in 2016 lasting 7 to 25 days. The objective of this paper is to provide insight on how transit disruptions affect bikeshare use. An autoregressive Poisson time series model is used to estimate effects of transit closures on bikeshare activity. Kernel density estimation is applied to understand spatial changes in ridership from a week before, one year before, and after each closure. Results are compared both temporally and spatially and confirm that transit disruptions were associated with increased bikeshare ridership at the local level. Once the affected Metro stations reopened, bikeshare ridership returned to original levels. We conclude that when within 0.25 mile of a rail station and with a rail station spacing of <3 miles, bikeshare can be used as a mechanism for low-carbon mobility to complement transit. [ABSTRACT FROM AUTHOR]

Ridesourcing, or on-demand ridesharing , is quickly changing today's travel. Recently, research has linked socio-demographics to ridesourcing use. However, little of the research has focused on the impacts of built environment, an important factor to consider in understanding travel behavior. This study applied Geographically Weighted Poisson Regression (GWPR) and examined the spatial relationships between built environment and ridesourcing demand. We used 2016–2017 ridesourcing trip data from a transportation network company (TNC), RideAustin , in Austin, Texas. By capturing the spatial heterogeneity, the GWPRs considerably improve modeling fit compared to the global models. Modeling results present strong relationships between ridesourcing demand and built environment variables (i.e., density, land use, infrastructure, and transit accessibility). More importantly, the results demonstrate significant spatial variations of the effects of both built environment and socio-economic variables and geographic trends from urban to suburban neighborhoods. Overall, these findings suggest that built environment factors have significant impacts on ridesourcing demand, and it is important to consider the spatial context. The study provides useful insights for understanding ridesourcing use as a function of built environment and have important implications for transportation planning, demand modeling, and urban governance. [ABSTRACT FROM AUTHOR]

Abstract Place-based accessibility measures, such as the gravity-based model, are widely applied to study the spatial accessibility of workers to job opportunities in cities. However, gravity-based measures often suffer from three main limitations: (1) they are sensitive to the spatial configuration and scale of the units of analysis, which are not specifically designed for capturing job accessibility patterns and are often too coarse; (2) they omit the temporal dynamics of job opportunities and workers in the calculation, instead assuming that they remain stable over time; and (3) they do not lend themselves to dynamic geovisualization techniques. In this paper, a new methodological framework for measuring and visualizing place-based job accessibility in space and time is presented that overcomes these three limitations. First, discretization and dasymetric mapping approaches are used to disaggregate counts of jobs and workers over specific time intervals to a fine-scale grid. Second, Shen's (1998) gravity-based accessibility measure is modified to account for temporal fluctuations in the spatial distributions of the supply of jobs and the demand of workers and is used to estimate hourly job accessibility at each cell. Third, a four-dimensional volumetric rendering approach is employed to integrate the hourly job access estimates into a space-time cube environment, which enables the users to interactively visualize the space-time job accessibility patterns. The integrated framework is demonstrated in the context of a case study of the Tampa Bay region of Florida. The findings demonstrate the value of the proposed methodology in job accessibility analysis and the policy-making process. Highlights • Studying job accessibility is important to urban planning and economic development. • Conventional job accessibility measures fail to consider the temporal dynamics. • A methodological framework is developed to measure and visualize place-based space-time job accessibility. • A case study of the Tampa Bay region of Florida suggests the value of the framework. • The framework can be adopted by other time-sensitive applications. [ABSTRACT FROM AUTHOR]

Abstract A key goal of urban transportation planning is to provide people with access to a greater number of opportunities for interaction with people and places. Measures of accessibility are gaining attention globally for use in planning, yet few studies measure accessibility in cities in low-income countries, and even fewer incorporate semi-formal bus systems, also called paratransit. Drawing on rich datasets available for Nairobi, Kenya this analysis quantifies place-based accessibility for walking, paratransit, and driving using three different measures: a mobility measure quantifying how many other locations in Nairobi can be reached in 60 min, a contour measure quantifying the number of health facilities that can be reached in 60 min, and a gravity measure quantifying the number of health facilities weighted by a time-decay function. Health facilities are used because they are an essential service that people need physical access to and as a representation of the spatial distribution of activities more broadly. The findings show that place-based accessibility is highest for driving, then paratransit, then walking, and that there are high levels of access to health facilities near the Central Business District (CBD) for all modes. Additionally, paratransit accessibility is comparatively better in the contour and gravity measures, which may mean that paratransit is efficiently providing access based on the spatial distribution of services. The contour measure results are also compared across different residential levels, which are grouped based on neighborhood characteristics and ordered by income. Counterintuitively, the wealthiest areas have very low levels of place-based accessibility for all modes, while poor areas have comparatively better walking access to health facilities. Interestingly, the medium low residential level, characterized in part by tenement apartment buildings, has significantly higher accessibility than other residential types. One way to reduce inequality in access across income groups is to increase spatial accessibility for the modes used by low- and middle-income households, for example with policies that prioritize public transport and non-motorized travel, integrate paratransit with land use development, and provide safe, efficient, and affordable options. [ABSTRACT FROM AUTHOR]

Abstract The determinants of car use and car ownership are heatedly discussed in transportation planning research. However, little is known about the effects of life-course events, for instance, residential relocation and changes in household structure, on driving behaviour. In particular, in China's cities, massive urban renewal and residential relocation provide new contexts for transport. This paper aims to contribute to new evidences for the links between changes in car use and residential relocation by taking Beijing as a case. Structural Equation Modelling is applied to explore the effects of built environment, self-selection, and household status on car ownership and car use, and to highlight the mediating effect of changes in commuting distance on travel mode variation following residential relocation. The results show that residents make their travel choices based on their life stages, that is, the birth of kids, marriage, and a larger household size encourage car purchase and facilitate car use. The built environment variables play moderate roles in reshaping driving behaviour after residential relocation when controlling for other factors. Besides, commuting distance has marginal effects on car use and travel mode choice. More specifically, increasing commuting distance encourages the use of the metro and private cars. The present study has important implications for future research and policies aiming to discourage car dependence in growing metropolises. Highlights • Residents make their travel choices based on their life stages • A larger household size encourage car purchase and facilitate car use. • The built environment variables play moderate roles in reshaping driving behaviour after residential relocation. • Increasing commuting distance encourages the use of the metro and private cars. [ABSTRACT FROM AUTHOR]

Understanding how the built environment influences e-scooter sharing (ESS) travel behavior is essential for informing effective built environment modifications to promote ESS usage. This study introduces a novel approach by utilizing link flow data, a different type of data compared to previous studies that focused on trip origin or destination information. By incorporating route-based built environment features and employing the gradient boosting decision tree (GBDT) method, we investigate the non-linear relationship between the built environment and ESS link flows. Our findings reveal the five most important variables affecting ESS travel behavior: distance to the city center, types of bike facilities, slope, speed limits, and street trees. Notably, bike facilities with higher levels of physical barriers from vehicle traffic attract more ESS link flows. Additionally, our results affirm the effectiveness of San Francisco's 20 mph commercial corridors in attracting ESS link flows. Our study generates new insights into the influences of the built environment on ESS travel behavior, contributing valuable knowledge that can be used to guide urban design and transportation planning efforts. • This study adopts link flow data to measure how the built environment affects ESS travel behavior. • The gradient boosting decision tree (GBDT) method is adopted. • The top five predictors are distance to the city center, bike facilities, slope, speed limits, and street trees. • Bike facilities with higher levels of physical barriers from vehicle traffic attract more ESS link flows. • 20 mph and 30 mph speed limits are the most popular choices, whereas 25 mph is the least favorable one. [ABSTRACT FROM AUTHOR]

Walkability remains a guiding framework for practitioners to design vibrant and healthy communities through urban planning interventions. While associations between travel behavior and physical factors in urban form have been confirmed through numerous studies, recent research underscores that focusing on the built environment poorly accounts for the sociodemographic factors bracketing amenity and route choices across space and time. As transportation planning turns toward strategies associated with maximizing accessibility over minimizing travel time, this epistemological gap is problematic for practitioners tasked with reducing structural inequalities in access to opportunity across society. Using participant testimony from a neighborhood in transition, as grounded in a qualitative geographic information system, we develop a user-oriented walkability framework which incorporates social and personal factors mediating pedestrians' spatial engagement. We advance from this framework to propose non-engineering interventions necessary to facilitate spatial engagement and, in turn, maximize walkable opportunities available to residents. [ABSTRACT FROM AUTHOR]

Transit-oriented development (TOD), first coined and mainly applied in the US, has received increasing interest in Europe over the past two decades as a sort of reinterpretation of typical intrinsic European planning principles and values. In the Old continent, it especially focuses on transport and land use integration, extending the approach from the local to the regional framework. This regional shift implies an intergovernmental cooperative approach with vertical and horizontal institutional coordination that constitutes both a strategic value and one of the main barriers to the implementation of TOD. This paper focuses on the role of regional planning for TOD by analysing its benefits, tools and barriers, both theoretically and through two European case studies. The latter, which were conducted in the Netherlands and in Italy, were selected as they belong to two different “planning families”, in order to underscore how and to what extent they apply this multi-level governance approach and overcome institutional barriers to the implementation of TOD. The analysis reveals that, albeit the two planning styles lead to different results, the main issue remains the deep coordination of land use and transport, which is hard to achieve despite dedicated efforts. [ABSTRACT FROM AUTHOR]

It is well known that journey-to-work (JTW) data can be represented using complex network graphs. What is less evident is the way in which this approach can be used to quantitatively analyse the structure, connectivity and dynamics of commuting behaviour. This paper employs a complex network approach to spatially disaggregated JTW data in order to examine commuting behaviour for six different modes of transport (car, car passengers, train, bus, cycling and walking) within three of the most populous metropolitan areas in Australia. A set of network measures ( degree, strength, clustering coefficient , maximum cliques , average shortest path length and betweenness ) are computed from both the unweighted and weighted graphs corresponding to JTW data for the Sydney, Melbourne and the South East Queensland regions from the time periods: 2001, 2006 and 2011. Results reveal a number of interesting dynamics, one being that Melbourne exhibits shorter (and presumed to be faster) alternate commuting paths than either Sydney or South East Queensland given its lower betweenness and shortest path values allied with higher clustering coefficients. The interpretation of these metrics demonstrates that complex networks have the capacity to reveal new insights from JTW data, by enabling a more comprehensive, systematic, empirical and fine-grained analysis of changes in commuting behaviour over time. [ABSTRACT FROM AUTHOR]

The spatial–temporal accessibility of a transport system assesses the spatial–temporal constraints faced by individuals based on their fixed activities and the ability of the transport system to facilitate trading time for space in movement. Previous studies either measured the individual spatial–temporal accessibility of a general transport network or measured the cumulative-opportunity accessibility by public transit through an exclusive computation of the bottom of the full network time prism. By contrast, the current study measures the individual spatial–temporal accessibility by public transit by integrating timetable and passenger departure time and computing the full network time prism. A public transit network is modelled as a time-dependent weighted directed graph, wherein every single directed arc is associated with a time-dependent travel time to represent the linkage between two adjacent stops on a transit route. The time-dependent travel time assigned to arcs is determined according to timetables, which is particularly assigned to infinity when transit service is unavailable between two stops. A modified network potential path area (N-PPA) algorithm based on the time-dependent weighted directed graph is employed to produce a potential path area for the activity participation of an individual by public transit. The proposed methodology is applied as a case study to measure individual spatial–temporal accessibility using the Salt Lake City TRAX system. Results indicate that the outcome of the proposed methodology is sensitive to departure time of passengers. The results of this study provide suggestions on potential improvement of bus/rail line layout and timetables and may aid in trip planning of passengers. [ABSTRACT FROM AUTHOR]

How and why commuting contributes to our well-being is of considerable importance for transportation policy and planning. This paper analyses the relation between commuting and subjective well-being by considering several cognitive (e.g., satisfaction with family life, leisure, income, work, health) and affective (e.g., happiness, anger, worry, sadness) components of subjective well-being. Fixed-effects models are estimated with German Socio-Economic Panel data for the period 2007–2013. In contrast to previous papers in the literature, according to which commuting is bad for overall life satisfaction, we find no evidence that commuting in general is associated with a lower life satisfaction. Rather, it appears that longer commutes are only related to lower satisfaction with particular life domains, especially family life and leisure time. Time spent on housework, child care as well as physical and leisure activities mediate the association between commuting and well-being. [ABSTRACT FROM AUTHOR]

The activity space of residents is an important mediator for the exploration of the relationship between residents' activities and the city. In this study, one-month mobile phone data are used to study the activity space of residents from three aspects, namely the radius of the activity space, the frequency of activity, and the diversity of activities. Combined with census data and point-of-interest data, a random forest model is established, and partial dependency plots are used to explore the non-linear effects of the built environment and socio-demographic attributes on the activity space. After analyzing mobile phone data from the central area of Shanghai, China, over one month, it is found that the facility density, accessibility, location, housing conditions, and marital status are the most important factors affecting the activity space of residents. Different from previous studies, it is found that the facility density and accessibility have a diminishing marginal effect on the activity space, and both the built environment and socio-demographic attributes have threshold effects on the activity space. The location relative to the Huangpu River is also found to influence the frequency of activity. Moreover, there are obvious differences between the activity spaces of home-owning residents and tenants. Among tenants, a too-low or too-high monthly rent can lead to a small radius of activity space and a low diversity of activities. Married residents often have a large radius of the activity space, a high frequency of activity, and a high diversity of activities. Some factors are also found to affect the influences of other factors on the activity space, and can even change the direction of correlation. The findings of this study can provide references for urban and transportation planning. • Mobile phone data are fused with POI and census data to study activity space. • Random forest and partial dependency plot are used to study non-linear effects of influencing factors on activity space. • Both built environment and socio-demographics have threshold effects on activity space. • Some factors are found to affect the influences of other factors on activity space. [ABSTRACT FROM AUTHOR]

Exploring the patterns of urban on-road (OR) emissions and their relationship with built environment (BE) is essential for urban planning oriented by transportation carbon emission reduction. This study quantified the road segment OR emissions with high spatial resolution by incorporating massive vehicle trajectory big data and vehicle type data in the metropolis, Shenzhen city in China. Multi-source big data such as Mobile signaling data, points of interest (POIs) data, were combined to characterize the BE factors at a fine spatiotemporal scale. This was integrated with GWR model to detect the spatial non-stationarity of the BE effects on OR carbon emissions at the transportation analysis zone (TAZ) level, which fills the research gap of the unclear effects of different BE unit on OR emissions reduction. Especially, we firstly assessed the timing feature of the resident movement by developing an improved actual jobs-housing balance (JHB) indicator supported by the mobile signaling data. The results showed that the actual JHB ratio and distance to transit were the most significant positive factors that influenced the emissions of most of the road segments. It provided implications for the policy makers with tradeoff among the instruments of possible BE improvements. Moreover, the TAZs were classified into three groups according to their coefficients in the GWR model. The clustering results have provided zoning policy implication for urban planning. [ABSTRACT FROM AUTHOR]

Planning organizations are increasingly looking to accessibility measures to understand how well the transportation system provides access to employment opportunities. However, the most common type of job accessibility measure employed in planning practice, the cumulative opportunity measure, considers job supply but overlooks competition for jobs. Therefore, the cumulative opportunity measure may not fully capture workers' access to job opportunity. In this paper, we examine four accessibility measures, two of which account for spatial competition and the other two do not, using the Los Angeles metropolitan area as a case study. We find that measures of competitive accessibility have stronger associations with employment than non-competitive accessibility measures. We examine the relationship between residential accessibility and employment for four separate levels of educational attainment, and we find that competitive measures are especially pertinent for population segments with lower educational attainment. Therefore, we recommend competitive accessibility measures to assess the employment opportunity more accurately. [ABSTRACT FROM AUTHOR]

Within a random utility maximisation modelling framework, the paper develops a residential location choice model as part of an integrated transport and land use modelling system, called MetroScan – a quick scanning tool to evaluate transport and land use initiatives, including benefit-cost analysis and economic impact analysis. We describe how the developed model is integrated, as an empirically calibrated module, into the behaviourally richer transport and land use modelling system of MetroScan for practical application. A full application of MetroScan modelling system to Sydney West Metro link recently proposed by the New South Wales government is presented as a case study. The results demonstrate how the residential location choice model works with other inter-connected models, such as work and non-work location choices, dwelling tenure and dwelling type, and vehicle fleet size choice embedded in the modelling system, in simulating the impact of transport and housing development on household choices of residential location. [ABSTRACT FROM AUTHOR]

Accessibility, the ease of reaching destinations, is a key land use and transportation performance measure that has been studied for decades by researchers. Nevertheless, its use by practitioners is generally limited. The goal of this study is to explore the challenges and opportunities experienced by land use and transportation practitioners to use accessibility metrics in their work. In order to achieve this objective, a survey on the use of accessibility metrics was conducted among 343 practitioners around the world. Findings from the survey show a gap between knowledge of the concept of accessibility and its use by land use and transportation practitioners. While 90% of the respondents are familiar with the concept, only 55% stated that they use accessibility metrics in their work. Whereas lack of support and interest does not appear to be a major obstacle to using accessibility metrics, lack of knowledge and data are highlighted as the main barriers to the use of metrics in practice. These results suggest that further training and collaboration is required to support the use of metrics by practitioners. Furthermore, including clear accessibility indicators in planning documents is key to promoting the use of metrics in policy and practice, as it was stated as a main reason motivating the generation of accessibility metrics. This research highlights potential avenues to support the integration of accessibility metrics in practice and is of relevance to researchers, planners and policy-makers wishing to foster accessibility-based planning approaches. [ABSTRACT FROM AUTHOR]

Accessibility, or the ease to participate in activities and obtain resources in a given environment, is crucial for evaluating transportation systems. Greater accessibility is often achieved by increasing individuals' potential mobility. However, potential mobility, if realized by motorized modes, can also generate negative environmental impacts such as fossil fuel consumption and greenhouse gas (GHG) emissions. While the negative environmental impacts of greater mobility are acknowledged, there has been a lack of research to validate those impacts using empirical data, especially considering variations in individuals' mobility levels. This paper presents a method for estimating the expected environmental costs of accessibility represented by a network-time prism (NTP). A NTP delimits all accessible locations within a network and the available time for an individual to present at each location given a scheduled trip origin and destination, a time budget and the maximum achievable speeds along network edges. Estimating the expected environmental costs of a NTP involves three steps: (1) semi-Markov techniques to simulate the probabilities to move along network edges at given times; (2) the speed profiles for reachable edges, and (3) a cost function that translates speeds into environmental impacts. We focus on air quality and employ the motor vehicle emission simulator MOVESLite to estimate the CO 2 emissions at both the edge and prism levels. We calibrate and validate the methods for experimental NTPs defined within the Phoenix, AZ, USA road and highway network using vehicles instrumented with GPS-enabled onboard diagnostic devices (OBD). We demonstrate the effectiveness of our method through two scenarios and investigate the impact of changes in mobility levels on the expected CO 2 emissions associated with the experimental NTPs. [ABSTRACT FROM AUTHOR]

Using a panel regression model and a multidimensional three-week household time-use and activity diary, this study analyses the complexity of the day-to-day variability in individuals' activity-travel patterns by applying a multidimensional sequence alignment model. It is found that the variability between weekend and weekday pairs is much greater than between weekday-weekday pairs or weekend-weekend pairs. The variability of other household members' activity-travel patterns is found to significantly influence an individual's activity-travel patterns. The results also show that the variability in the activity-travel patterns of workers and students is greater when conducting a particular discretionary activity on weekdays. Due to performing discretionary activities more often and for longer, non-workers tend to have more predictable activity-travel patterns. Undertaking multitasking activities within different activities on weekdays significantly impacted the different degrees of variability in an individual's activity-travel patterns. Having different health and built environment characteristics also corresponds with different degrees of predictability of the activity-travel patterns, particularly in the worker/student case. [ABSTRACT FROM AUTHOR]

Most studies on walking distance to transit stops either emphasize transit access or do not distinguish transit access and egress. Furthermore, environmental correlates of walking distance may differ by stop location. Using the 2010 Transit Onboard Survey in the Minneapolis and St. Paul Metropolitan Area, this study develops four models to compare the effects of the built environment around transit stops on walking distance of transit egress. Job density is negatively correlated with walking distance, consistent in all four models. Other built environment variables exhibit different impacts by stop location. Particularly, land use mix has positive impacts on walking distance for stops outside of downtown and suburban employment centers whereas job density is more important for suburban centers. Job accessibility and the number of intersections have significant effects on stops within downtown areas but have no significant impacts on stops outside of downtown areas. The number of transit stops has opposite impacts on walking distance for stops within and outside of downtown. Moreover, the built environment tends to have a larger impact on walking distance in downtown areas than non-downtown areas. We then discuss the implications for stop area land use planning and transit stop location choice. [ABSTRACT FROM AUTHOR]

Geographically weighted regression (GWR) has been increasingly used to better understand the spatially varied relationships between socioeconomic outcomes and policy investments. In this study, a community opportunity index (COI) is computed for Columbus, Ohio, using a set of socioeconomic factors. An ordinary regression and GWR model are estimated to account for the global and local effects of land uses and capital investments respectively, while controlling for socio-demographical characteristics. The global results indicate that the community opportunity increases as the distance from the city center increases, while The Ohio State University (OSU) has higher positive spillover-effect on near communities than on distant ones. However, the local results appear a local spatially inverse relationship in the areas adjacent to the international airport (CMH), indicating the existence of negative externalities. With the advantage of visualizing spatial variations, the GWR results suggest that the most effective location for allocating future developments is in eastern Columbus, where shows a clustering of higher COI premiums of a percentage change in residential and commercial uses. A variety of spatial variations is found among different capital-investment effects. Therefore, local characteristics require consideration when allocating additional public facilities. Finally, the GWR results reveal the existence of spatial mismatch that socially disadvantaged groups (e.g. black population, other minorities, single-parent families, and zero-vehicle households) tend to reside in vulnerable communities. These local results provide a new perspective on land-use and transportation planning to help shape a fair community opportunity framework for the future. [ABSTRACT FROM AUTHOR]

The regular and robust collection of traffic data for the entire road network in a given country will usually require high-cost investment in traffic surveys and automated traffic counters. This paper provides an alternative and low-cost approach for estimating annual average daily traffic values (AADTs) and the associated transport emissions for all road segments in a country. This is achieved by parsing and processing commonly available information from existing geographical data, census data, traffic data and vehicle fleet data. Ceteris paribus, we find that our annual average daily traffic estimation based on a neural network performs better than traditional regression models, and that the outcomes of our aggregated bottom-up road segment emission estimations are close to the outcomes from top-down models based on total energy consumption in transport. The developed approach can serve as a means of reliably estimating and verifying national road transport emissions, as well as offering a robust means of spatially analysing road transport activity and emissions, so as to support spatial emission inventory compilations, compliance with international environmental agreements, transport simulation modelling and transport planning. [ABSTRACT FROM AUTHOR]

Road network patterns influence traffic operational performance and road safety. A precise description of road network patterns can provide useful guidance for both design and improvement of road systems. Most previous studies have classified road network patterns using a visual inspection method. Although visual inspection is valuable in practical applications, it can be subjective and time consuming. Hence, a more reliable, automated, and quantitative approach for the classification of road network patterns is required. The objective of this study is to develop and apply a quantitative method for network pattern classification. Data analyzed are from 718 traffic analysis zones (TAZs) in Florida's Hillsborough County. Six quantitative metrics, geometric and topologic, were analyzed, and their ability to classify different road network patterns were compared. Then, a multinomial logit model was developed to classify various network patterns using the six metrics. The results show that meshedness coefficient, proportion of cul-de-sacs, and proportion of 4-legged intersections were the three most significant variables in determining network patterns. Finally, this quantitative method was validated using TAZ data from Florida's Orange County, with an accuracy of 74.7%. This accuracy demonstrates the potential of the method in automatically and reliably classifying road network patterns. [ABSTRACT FROM AUTHOR]

The backcasting approach is being increasingly used in the field of transport to address issues of climate change. While it acknowledges that a structured involvement of stakeholders should become central in transport backcasting studies, there are very few policy relevant papers that pay special attention to stakeholder participation in the visioning phase of backcasting. This paper aims at showing the findings of a participatory visioning study as a starting point of a wider backcasting analysis for the transport sector (2050) in Andalusia (Spain). It presents a methodological approach that involves a total of 40 stakeholders and combines two participatory techniques: (i) Delphi survey; (ii) semi-structured interviews. The main outcomes show how stakeholders were engaged in the participation process through each technique. It then identifies five relevant methodological issues for a more detailed discussion: (i) the selection of participants; (ii) the means to visualise long-term futures; (iii) the visualisation of desired futures; (iv) the generation of multiple future visions; (v) the combination of multiple participatory techniques. In parallel, the study also presents the means by which the use of both participatory techniques can provide a narrative of a future vision for the transport sector in Andalusia. That vision focuses largely on lower carbon emissions, technological innovation, and urban compactness. [ABSTRACT FROM AUTHOR]

Bus lines play an essential role in providing urban public transport service. Demographic and socioeconomic characteristics determine the specific transport service needs of districts that, in most cases, are not served by the existing bus routes. Small changes in the frequency of a bus route can have a direct effect on the territorial distribution of a city's public transport service. Adjusting the frequencies of a bus fleet can help planners improve the service level and adapt the fleet's spatial distribution. A bus frequency optimization methodology is proposed to improve harmonization between service level and social equity in public transport. The method relies on a simple spreadsheet that integrates geographic data about bus routes and socioeconomic information about city districts. Simulation and optimization procedures are coordinated to appropriately adjust the routes to the geographic space. The proposed method is tested in a real case study that demonstrates its usefulness in analysing the service level and equity of a transport system and improving a city's public transport planning. [ABSTRACT FROM AUTHOR]

Spatial network analysis (SpNA) provides a promising alternative to traditional transport models for the modelling of active travel, because walking and cycling behaviour is influenced by features smaller than the scale of zones in a traditional model. There is currently a need for link-level, city wide modelling of cycling, both to ensure the needs of existing cyclists are catered for in planning, and to model the effects of changing infrastructure in shaping cyclist behaviour. Existing SpNA models treat cyclists and car drivers as if they make navigational decisions in a similar way, which in reality is not the case. This paper presents an SpNA model using hybrid betweenness, which fits cyclist flows in Cardiff, Wales using distance, angular distance, motor vehicle traffic and slope as predictors of route choice. SpNA betweenness is also shown to implicitly capture the effect of urban density on mode choice. As it handles route finding decisions of drivers and cyclists separately, the model presented is also applicable to road safety models examining the interaction between the two classes of road user. The model has low cost of data collection and is reproducible using publicly available network analysis software and open mapping data. Further avenues for modelling the effect of infrastructure on cycling are discussed. [ABSTRACT FROM AUTHOR]

While adequate integration of land use and transport is seen as crucial for achieving sustainable outcomes, the reciprocal interconnection between retail activity and non-motorised accessibility is yet to be adequately examined. To address this gap, this paper proposes the concept of Retail Mobility Environment (RME) and develops a methodological process for identifying and mapping RMEs, using the city of Zaragoza, Spain, as a case study. The concept of RME is developed through three methodological phases: (i) definition of non-motorised Accessibility Zones (AZs), using three indicators (walking accessibility, bicycling accessibility, betweenness); (ii) definition of Retail Zones (RZs), also using three indicators (retail density, retail diversity, retail contiguity); and (iii) definition of RMEs, where both retail activity and non-motorised indicators were weighted and combined using multi-criteria analysis. In total, four RMEs were identified and mapped: short-distance environments, motorised environments, non-motorised environments, and long-distance environments. The paper concludes with a discussion on the need to unravel the relationships between retail activity and non-motorised accessibility, in order to reach sustainable planning goals, as well as the potential usefulness of RMEs for transport policy-making. [ABSTRACT FROM AUTHOR]

In an ideal world, transportation networks and services would be adapted to the specific travel needs of each individual and would perfectly fit the corresponding desire lines (direct lines between origin and destination points). However, in practice, networks cannot be designed to accommodate each individual trip. Still, it is possible to optimize transportation systems from a collective demand point of view. To move from an individual to a collective scale, individual demands need to be encapsulated into demand corridors. Although current spatial tools and data mining techniques are able to identify corridors from numerous movements by using linear or non-linear trajectory data, their limitations—from a transportation point of view—include the use of non-intuitive parameters and the application of some aggregation processes that make it difficult to retrace the attributes of individual input data that could benefit the richness of the available data after processing. For that reason, we propose a new algorithm called Trajectory Clustering for Desire Lines (TraClus-DL), which can identify corridors from Origin-Destination (OD) information with simple parameters, such as spatial location, angles between lines, and sampling weights. The functionality of TraClus-DL as a diagnostic tool for transportation supply was assessed and tested using data from the 2013 OD travel survey conducted in the Montreal area. The sensitivity of the results, with respect to parameter settings, was evaluated, and a comparison with an existing algorithm was proposed. The results of this study demonstrate that transportation specialists can benefit from the convenience of using TraClus-DL as a corridor identification tool, which includes its potential to perform deep analyses at the corridor level. In addition, this study provides new insights into the possible uses of demand corridors as relevant tools for transportation planning, and in the decision-making processes in which a neutral reference is needed to evaluate how much the transportation supply differs from the collective travel demand. [ABSTRACT FROM AUTHOR]

Effective transportation planning necessitates the consideration of all road network users and their needs. Towards this goal, the integration of accessibility in planning and the development of tools that enable the assessment and measurement of accessibility within urban areas becomes essential. This study aims to contribute to the accessibility assessment process of urban areas by developing two accessibility indices: the infrastructure and the opportunity accessibility index, which use an infrastructure-based and distance-based approach, respectively. Four types of users and their needs are considered: pedestrians, People with Disabilities (PWD), cyclists and public transport users. Data for modeling the accessibility indices are collected by: 1) an infrastructure audit, 2) a user survey, and 3) geographic information systems. The proposed method is applied in a district in central Athens, Greece, which is characterized by high population density and high level of activities. The Infrastructure Accessibility Index (IAI) measures accessibility for sidewalks, crosswalks, bikeways and public transport stops. The IAI results indicate moderate accessibility levels for pedestrians and PWD, for sidewalks and crosswalks. The Opportunity Accessibility Index (OAI) measures the share of different types of users that reach different opportunities within a time threshold. The accepted time threshold per user type is estimated based on the survey responds, for seven different opportunities: green spaces, recreational spaces, education buildings, health buildings, public service building, commercial uses and public transport stops. Compared to the IAI, the majority of users reach different opportunities within the estimated time threshold. The study concludes with recommendations to improve accessibility levels at local level. [ABSTRACT FROM AUTHOR]

Transport is a significant and growing contributor to climate change. To stay within ‘safe’ global warming guardrails requires substantial cuts in greenhouse gas emissions. This represents a global political consensus, but there is evidence that current legislation in the transport sector is not significant enough to achieve medium- and longer-term reduction goals. In focusing on the European Union, this paper investigates the perspectives of twelve policy officers in three Directorates-General (MOVE, CLIMA, ENV) of the European Commission with regard to their understanding of mitigation goals and timelines, responsibilities for policy development and implementation, and perceived efficiencies of these policies to achieve climate objectives in the transport sector. Results indicate diverging and common views on climate policy goals and political responsibilities, as well as barriers to policy-making, including lack of political leadership on climate change mitigation, resistance from member states, the favoring of economic growth over cuts in greenhouse gas emissions, pressure from industry and lobby groups, preferential treatment of aero- and automobility over more sustainable transport modes, policy implementation delays, insufficient forecasting and monitoring tools, and an overreliance on technologies to contribute to emission reductions. In offering a view inside the ‘black box’ of transport policy-making, the paper reveals fundamental institutional (structural) and individual (agency-based) barriers that will have to be overcome if significant emission cuts in the transport sector are to be achieved. [ABSTRACT FROM AUTHOR]

Mobility hubs are increasingly mentioned in urban planning and transportation policies as a potential solution for keeping urban areas accessible in the context of growing population and promotion of sustainable mobility. Yet, an overall academic conceptualisation of mobility hubs in the form of a typology is still lacking. This study contributes to literature by identifying six types of mobility hubs by the method of grounded theory. This study is focused on the recent Dutch planning experience: data were gathered by conducting 16 expert interviews, one panel discussion, and a text analysis of 33 policy and planning publications. A conceptual framework is developed by proposing a typology of mobility hubs. We conclude discussing some of the major challenges policy makers and planners face in the development of mobility hubs. [ABSTRACT FROM AUTHOR]

This paper investigates whether different types of accessibility measures can perform similarly when evaluating changes in accessibility before and after transport interventions. Specifically, we compare three types of placed-based accessibility approaches: 1) cumulative-opportunity, 2) gravity-based, and 3) hybrid of the two. We conduct a comprehensive comparison of 12 accessibility measures by combining different operational forms, impedance functions, and parameters to see if the results of project appraisals are sensitive to the choice of accessibility measure. For case studies, we consider two scenarios in Canada: new bus rapid transit (BRT) services in Vancouver and transit system redesign (TSR) in Edmonton. We carry out high spatio-temporal resolution accessibility analyses based on General Transit Feed Specification (GTFS) datasets through an open-source transit analytics tool. Results demonstrate that the three types of measures perform differently when evaluating changes in accessibility pre and post transit interventions. Accessibility change scores generated by cumulative-opportunity measures are notably different from those produced by gravity-based and hybrid measures. Also, the capability of cumulative-opportunity approach in capturing the geographic extent of areas affected by transit interventions is limited. We found that the project appraisal results can even change from positive to negative or vice versa, depending on the chosen accessibility measure. We recommend an analyst test several types of accessibility measure with a range of parameters for a more robust evaluation on the impacts of new public transit services on accessibility. We share our code and data to promote reproducible transportation science in planning practice. [ABSTRACT FROM AUTHOR]

Building an accurate model of travel behaviour based on individuals' characteristics and built environment attributes is of importance for policy-making and transportation planning. Recent experiments with big data and Machine Learning (ML) algorithms toward a better travel behaviour analysis have mainly overlooked socially disadvantaged groups. Accordingly, in this study, we explore the travel behaviour responses of low-income individuals to transit investments in Greater Toronto and Hamilton Area, Canada, using statistical and ML models. We first investigate how the model choice affects the prediction of transit use by the low-income group. This step includes comparing the predictive performance of traditional and ML algorithms and then evaluating a transit investment policy by contrasting the predicted activities and the spatial distribution of transit trips generated by vulnerable households after improving accessibility. We also empirically investigate the proposed transit investment by each algorithm and compare it with the city of Brampton's future transportation plan. While, unsurprisingly, the ML algorithms outperform classical models, there are still doubts about using them due to interpretability concerns. Hence, we adopt recent local and global model-agnostic interpretation tools to interpret how the model arrives at its predictions. Our findings reveal the great potential of ML algorithms for enhanced travel behaviour predictions for low-income strata without considerably sacrificing interpretability. [ABSTRACT FROM AUTHOR]

Zonal crash prediction has been one of the most prevalent topics in recent traffic safety research. Typically, zonal safety level is evaluated by relating aggregated crash statistics at a certain spatial scale to various macroscopic factors. Another potential solution is from the micro level perspective, in which zonal crash frequency is estimated by summing up the expected crashes of all the road entities located within the zones of interest. This study intended to compare these two types of zonal crash prediction models. The macro-level Bayesian spatial model with conditional autoregressive prior and the micro-level Bayesian spatial joint model were developed and empirically evaluated, respectively. An integrated hot zone identification approach was then proposed to exploit the merits of separate macro and micro screening results. The research was based on a three-year dataset of an urban road network in Hillsborough County, Florida, U.S. Results revealed that the micro-level model has better overall fit and predictive performance, provides better insights about the micro factors that closely contribute to crash occurrence, and leads to more direct countermeasures. Whereas the macro-level crash analysis has the advantage of requirement of less detailed data, providing additional instructions for non-traffic engineering issues, as well as serving as an indispensable tool in incorporating safety considerations into long term transportation planning. Based on the proposed integrated screening approach, specific treatment strategies could be proposed to different screening categories. The present study is expected to provide an explicit template towards the application of either technique appropriately. [ABSTRACT FROM AUTHOR]

There have long been calls for better pedestrian planning tools within travel demand models, as they have been slow to incorporate the large body of research connecting the built environment and walking behaviors. Most regional travel demand forecasting performed in practice in the US uses four-step travel demand models, despite advances in the development and implementation of activity-based travel demand models. This paper introduces a framework that facilitates the abilities of four-step regional travel models to better represent walking activity, allowing metropolitan planning organizations (MPOs) to implement these advances with minimal changes to existing modeling systems. Specifically, the framework first changes the spatial unit from transportation analysis zones (TAZs) to a finer-grained geography better suited to modeling pedestrian trips. The MPO's existing trip generation models are applied at this spatial unit for all trips. Then, a walk mode choice model is used to identify the subset of all trips made by walking. Trips by other modes are aggregated to the TAZ level and proceed through the remaining steps in the MPO's four-step model. The walk trips are distributed to destinations using a choice modeling approach, thus identifying pedestrian trip origins and destinations. In this paper, a proof-of-concept application is included to demonstrate the framework in successful operation using data from the Portland, Oregon, region. Opportunities for future work include more research on the potential routes between origins and destinations for walk trips, application of the framework in another region, and developing ways the research could be implemented in activity-based modeling systems. [ABSTRACT FROM AUTHOR]

Transit oriented development (TOD), which is generally understood as the provision of higher-density, mixed-use, amenity-rich, and walkable development around rapid transit stations, has been championed as one of the most effective solutions for maximizing the potential return on investment for existing and future rapid transit infrastructure projects. But it is clear that not all implementations of TOD are the same in every station catchment area across a transit network. This heterogeneity in station area contexts presents significant complexity for planners and policymakers interested in understanding existing TOD conditions, an area's TOD potential, and the relevant policy and planning interventions required to achieve planning goals. It also creates complications for researchers interested in associating station contexts with various TOD outcomes. In response, the present paper develops a model-based latent class method for distilling measures of station area TOD inputs into a set of more homogeneous station types. Its application to 372 existing and planned rapid transit stations in the Toronto region reveals a typology of 10 distinct TOD contexts across a number of present and future transit lines. The end result is an empirical tool for policy evaluation and prescription that can be used to benchmark and compare performance of TOD inputs around existing and planned transit stations and offers a foundation for further research into the relationship between TOD inputs and outcomes. Furthermore, the use of latent class analysis improves on the previous literature in this area by offering model results that are easily interpretable and extendable to other applications. [ABSTRACT FROM AUTHOR]

Cycling volumes are necessary to understand what influences ridership and are essential for safety studies. Traditional methods of data collection are expensive, time consuming, and lack spatial and temporal detail. New sources have emerged as a result of crowdsourced data from fitness apps, allowing cyclists to track routes using GPS enabled cell phones. Our goal is to determine if crowdsourced data from fitness apps data can be used to quantify and map the spatial and temporal variation of ridership. Using data provided by Strava.com , we quantify how well crowdsourced fitness app data represent ridership through comparison with manual cycling counts in Victoria, British Columbia. Comparisons are made for hourly, AM and PM peak, and peak period totals that are separated by season. Using Geographic Information Systems (GIS) and a Generalized Linear Model we modelled the relationships between crowdsourced data from Strava and manual counts and predicted categories of ridership into low, medium, and high for all roadways in Victoria. Our results indicate a linear association (r 2 0.40 to 0.58) between crowdsourced data volumes and manual counts, with one crowdsourced data cyclist representing 51 riders. Categorical cycling volumes were predicted and mapped using data on slope, traffic speeds, on street parking, time of year, and crowdsourced ridership with a predictive accuracy of 62%. Crowdsourced fitness data are a biased sample of ridership, however, in urban areas the high temporal and spatial resolution of data can predict categories of ridership and map spatial variation. Crowdsourced fitness apps offer a new source of data for transportation planning and can increase the spatial and temporal resolution of official count programs. [ABSTRACT FROM AUTHOR]

Although significant effort is being made in addressing infrastructure design, construction, operations, and maintenance, there is the need for assessing organizational sustainability within transportation planning. Transportation planners have identified coordination and collaboration as fundamental steps in addressing issues related to transportation network planning and sustainability initiatives. This research explores multi-jurisdictional collaboration between agencies using a case study on Pennsylvania Metropolitan/Rural Planning Organizations (MPOs/RPOs) and non-designated areas. The agencies are surveyed and the results are analyzed using a network analysis software, Gephi. In order to compare the collaboration network analysis (survey results) to influential factors, such as geographic adjacency and geographic proximity (reflective of transportation networks), GIS is used in combination with Gephi to complete geographical network analyses. The three analyses are compared using average degree, density, and average path length. The results indicate that the MPOs, RPOs, and non-designated areas within the state of Pennsylvania are collaborating, on average, beyond the geographical adjacency but below the level of geographical proximity network. In addition, email and phone communication forms are the most widely used for high frequency connection while face-to-face meetings are more likely for biannual and annual collaboration. The results of this study serve as a foundation for measuring and monitoring multi-jurisdictional collaboration to promote sustainable organizational planning in transportation. [ABSTRACT FROM AUTHOR]

The paper proposes a new behavioral definition of accessibility as the expected number of opportunities “available” for a subject to perform an activity, where “available” means that the opportunity i ) is perceived as a potential alternative to satisfy one's needs, and ii ) it can be reached given the spatiotemporal constraints of the individual's schedule. A new class of accessibility models is derived in accordance with the above definition, exploiting the strengths of both utility-based and opportunity-based models, and explicitly incorporating spatiotemporal constraints which may limit the availability of perceived opportunities. The proposed model is formulated for both active and passive accessibility, does not suffer from reflexivity issues, supports both trip-based and activity-based formulations, and scales up from individual to spatially aggregated opportunities, taking into account effectively the different ways of perceiving opportunities. Moreover, the resulting accessibility measure has a straightforward interpretation, being expressed in physical units, and is comparable across different locations. Performances of the proposed model in reproducing the active accessibility to cinemas in the Naples metropolitan area (Italy) are compared to those of a traditional isochrone-based measure and a distance–decay model. For this aim, measurements of individuals' perceived cinemas were gathered through a survey, and adopted for the calibration of both the accessibility models. Calibration results show that the proposed model outperforms both the traditional isochrone-based measure and a distance–decay model calibrated against the same dataset, better reproducing both the quantity and spatial distribution of perceived opportunities. [ABSTRACT FROM AUTHOR]

As the practices of transportation engineering and planning evolve from “data poor” to “data rich”, methods to automate the translation of data to information become increasingly important. A major field of study is the automatic identification of travel modes from passively collected GPS data. In previous work, the authors have developed a robust modal classification system using an optimized combination of statistical inference techniques. One problem that remains very difficult is the correct identification of transit travel, particularly when the system is operating in mixed traffic. This type of operation generates a wide range of values for many travel parameters (average speed, maximum speed, and acceleration for example) which have similar characteristics to other urban modes. In this paper, we supplement the previous research to improve the identification of transit trips. The method employed evaluates the likelihood that GPS travel data belong to transit by comparing the location and pattern of zero-travel speeds (stopping) to the presence of transit stops and signalized intersections. These comparisons are done in a GIS. The consideration of the spatial attributes of GPS data vastly improves the accuracy of transit travel prediction. [ABSTRACT FROM AUTHOR]