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Nature-inspired algorithms allow us to solve many problems related to the search for optimal solutions. One such issue is the problem of searching for optimal routes. In this paper, ant colony optimization is used to search for optimal tram routes. Ant colony optimization is a method inspired by the behavior of ants in nature, which as a group are able to successfully find optimal routes from the nest to food. The aim of this paper is to present a practical application of the algorithm as a tool for public transport network planning. In urban public transport, travel time is crucial. It is a major factor in passengers' choice of transport mode. Therefore, in this paper, the objective function determining the operation of the algorithm is driving time. Scheduled time, real time and theoretical time are analyzed and compared. The routes are then compared with each other in order to select the optimal solution. A case study involving one of the largest tramway networks in Poland demonstrates the effectiveness of the nature-inspired algorithm. The obtained results allow route optimization by selecting the route with the shortest travel time. Thus, the development of the entire network is also possible. In addition, due to its versatility, the method can be applied to various modes of transport. [ABSTRACT FROM AUTHOR]

Railways are a mode of public transportation that can carry large numbers of passengers and commodities and can cover long distances quickly. Based on these advantages, the railway is a mode of transportation that is in great demand by people as their choice of transportation. Thus, this will increase the intensity and frequency of departures, thereby triggering opportunities for errors to occur that threaten passengers' safety. Currently, the paper needs to present risk assessment activities in railways comprehensively. This paper will systematically review the literature on risk management concepts, risk assessment techniques, standards, and guidelines for risk management in railways. It also groups the literature to provide directions and recommendations for distances that future researchers can examine. This study encompasses a review of 60 publications on risk management, risk assessment, and risk analysis in railways from reputable and highly indexed journals over the last ten years. Subsequently, an analysis was conducted on the characteristics of models, methods, and techniques proposed in railway risk assessment. Several countries' standards, regulations, and guidelines on railway safety will also be described. The number of studies examining risk assessment at level crossings is minimal, whereas most accidents occur in this area. Many countries have legislation and guidelines but are still general and do not detail risk assessment activities in areas with a high level of risk. [ABSTRACT FROM AUTHOR]

Background: The processes of transport decarbonisation are complicated. In this paper, we adopt the Activity-Modal Share-Energy Intensity-Carbon Intensity of Fuel (ASIF) approach and propose a conceptual framework on the direct and indirect impact of COVID-19 on transport CO2 emissions. In the Chinese context, changes of carbon emissions associated with passenger and freight transport (including urban, rural, and inter-city transport) across different transport modes are estimated. Scenario analysis is then used to estimate the impact of COVID-19 on total transport carbon emissions up to 2030. Four scenarios, from minimal to significant behavioural changes and global recession associated with COVID-19, are generated. Results: Under the pandemic, the transport system in China was estimated to have produced 28% less CO2 emissions (1044.2 Mt) in 2020, when compared to 2019. Compared with the business-as-usual scenario, the estimated total transport carbon emissions in 2030 would drop by 6%, 15%, and 21% and 23% under the minimal-impact, low-impact, moderate-impact, and severe-impact scenarios, respectively. Conclusions: The results suggest that the processes triggered by COVID-19 alone will not be sufficient to meet the ambitious transport decarbonisation targets. To meet China's pledge under the United Nations Framework on Climate Change, the medium-term effects of COVID-19 must be combined with strong transport decarbonisation measures of modal shift and new energy applications. With these additional measures, it may be possible to advance the transport carbon peak before 2030. Lessons are relevant to other developing countries. [ABSTRACT FROM AUTHOR]

Over the past years, transport mode recognition has become a large field of research. However, flight as a type of transportation has been mostly overlooked. A system for flight detection might be useful for context-aware applications, but more importantly, it can be used to automatically manage airplane mode on smartphones. Smartphones transmit radio frequency signals which could potentially interfere with aircraft systems, and it is therefore important that devices enable airplane mode to avoid this problem. This paper proposes flyDetect, a method for automatic flight mode detection and an embodiment in the form of an app that demonstrates the viability of the method. Thus, the system uses the accelerometer and barometer in an Android smartphone, can detect the start and end of a flight, and notify other apps or systems on the device when this happens. Our evaluation shows that flyDetect meets the requirements set for the solution, and the results are very promising. [ABSTRACT FROM AUTHOR]

After the May 12 earthquake in Wenchuan, numerous debris flow disasters occurred due to heavy rainfall. After the earthquake, disaster prevention and mitigation projects, such as the sand dam, played an essential role in safety. However, when the outflow of solid material exceeds the designed storage capacity of the sand dam, the debris flow overtopping the dam intensifies the scraping effect on the source material of the gully channel. This paper takes the "8.20" debris flow in the Chutou gully as the research object. A transport mode of the debris flow after dam overtopping is studied through comprehensive field investigation, theoretical analysis, and indoor model tests. A quantitative evaluation model is established for the scraping depth and scraping volume of dam-overtopping debris flow. The study results showed that once the debris flow overtops a dam, the loose source carried by the channel is involved in the entire migration process, enlarging the fluid scale and intensifying the damage along the channel. The results agree with the measurements and observations, with maximum relative errors of 5.81% and 14.70%, respectively. This shows model validity. Furthermore, the test results show that the debris flow scarping phenomenon in the γ=[1.4 t/m3, 1.6 t/m3] interval is the most significant. Overall, this study will help improve future engineering decisions, design strategies, and finding the best design solutions to minimize the risk of dam-overtopping debris flow disasters. [ABSTRACT FROM AUTHOR]

The average daily throughput of large-scale passenger high-speed railway stations is large, and the design of the inbound space connecting with the underground and other modes of transport affects the passengers' wayfinding behaviour and time spent, which in turn affects the efficiency of the inbound station. How to optimise the design of station entry space and signage arrangement becomes the key to shortening the station entry time. In this paper, eye tracking, spatial syntax, and semantic difference methods are used to evaluate the passenger's wayfinding process in the underground hub of a large high-speed railway station and the spatial syntax is used to quantify and analyse the wayfinding path segments, to explore the influence of the spatial attributes of different nodes and the spatial arrangement of the guiding signs on the passenger's wayfinding behaviour data and the difference in attention, and to find out that the connectivity of the wayfinding nodes, the area of the field of view, and the passengers' The study concludes that the connectivity and visual field area of wayfinding nodes have a strong positive correlation with the passengers' route choice time, which has less influence on the correct rate of wayfinding and can be taken into less consideration in the subsequent design. While analysing the spatial density of signs and the correct rate of wayfinding in the sample, it is concluded that the density of guide signs is maintained in the interval of 5–11‰, and at the same time, the number is sufficient to point to the destination is a more appropriate interval, and ultimately, the impact of the correct rate of wayfinding of the weighting of the following: signage focus on the time > density of information > density of key information > diameter of the pupil. The study analyses the influencing factors affecting passengers' wayfinding behaviour from a human factors perspective and provides feedback on the design of underground entry spaces in large passenger high-speed rail stations. [ABSTRACT FROM AUTHOR]

In the framework of sustainable development, the most important goal in the transport sector is sustainable mobility. It is necessary to improve the quality of the means of transport and the organisation of their operation in transport systems in addition to the management systems, coordination, and integration of individual types of transport. The aim of this paper is to propose a complex system of public transport lines in the region to achieve an effective transport service, in accordance with the principles of sustainable mobility, supporting the development of an integrated transport system. This research presents a proposal for the public transport lines of the region of Prešov in Slovakia, which is currently connected to an integrated transport system (IDS Východ). The proposal is defined by the tracing and functions of the lines, the mode of transport and headways on individual lines, the necessary number of vehicles for the operation of individual lines, transfer times at relevant points and directions, and the border of the radius of attractive daily commuting to the centre of the region, as well as a rough timetable for each line. Using an analytical hierarchical process (AHP method), we determine the two most suitable variants of traffic services. The results indicate that based on the multicriteria evaluation of these variants using descriptors, it is possible to determine the optimal variant of the transport service of the region, which will be introduced into a functional integrated transport system. [ABSTRACT FROM AUTHOR]

Bikeshare, as a convenient transport mode, can address the first- and last-mile travel needs of metro trips while generating many environmental benefits, such as reducing the use of environmentally unfriendly transport modes and lowering the carbon emissions of the urban transportation system. This paper takes bikeshare as a feeder mode of metro stations (BS-FMMS) as the research object and compares the spatial and temporal differences in the carbon emission reduction benefits of BS-FMMS on workdays and non-workdays by using the framework of BS-FMMS carbon reduction benefit analysis and the methods of time-series analysis, spatial aggregation analysis, and box plot analysis. The results show that the carbon emission reduction benefit of bikeshare has obvious morning and evening peaks on workdays, while it tends to be stable without obvious peaks during the day on non-workdays. From the perspective of spatial distribution, the carbon emission reduction benefits of BS-FMMS are more significant in the metro station areas in the south of Baoan district, the west of Nanshan district, the central of Longhua district, and the south of Futian district in Shenzhen city, and the metro stations where the carbon emission reduction benefits of the non-workday are greater than those of the workday are mainly concentrated in Nanshan district, Futian district, and Luohu district. There is a significant positive correlation between BS-FMMS ridership and carbon emission reduction. These findings can provide clear policy implications for the decarbonization development of urban transportation systems. [ABSTRACT FROM AUTHOR]

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Existing research rarely examines the subjective and objective built environment of river valley-type cities in relation to travel mode choice, particularly overlooking the heterogeneity among travelers in these cities. In this paper, based on questionnaire survey data and built environment data, terrain spatial perception (TSP) is introduced to expand the theory of planned behavior (TPB), and a Structural Equation Model (SEM) is established. Factor analysis and path analysis are conducted using SPSS and AMOS to estimate latent variables. An integrated model of SEM and random parameter Logit model (RPLM), which can not only analyze the psychological perception factors of commuters in river valley-type cities but also consider the heterogeneity of psychological perception, was constructed to analyze the impact of personal attributes, objective built environment factors, and psychological latent variables on the commuting mode choice behavior of public transport users in river valley-type cities. The results indicate that the five observation indicators corresponding to the proposed terrain spatial perception latent variables can better explain the terrain spatial perception of commuters in river valley-type cities. Different from plain cities, the subjective and objective built environment of river valley-type cities notably influence the travel behavior of commuters. Moreover, the parameters of terrain spatial perception follow a normal distribution, indicating that the sensitivity of different commuters to the terrain spatial perception of river valley-type cities is heterogeneous. The results of our study can provide a reference for alleviating traffic issues in valley cities. [ABSTRACT FROM AUTHOR]

With the continuous development of large-scale engineering projects such as construction projects, relief support, and large-scale relocation in various countries, engineering logistics has attracted much attention. This paper addresses a multimodal material route planning problem (MMRPP), which considers the transportation of engineering material from suppliers to the work zones using multiple transport modes. Due to the overall relevance and technical complexity of engineering logistics, we introduce the key processes at work zones to generate a transport solution, which is more realistic for various real-life applications. We propose a multi-objective multimodal transport route planning model that minimizes the total transport cost and the total transport time. The model by using the ε − constraint method that transforms the objective function of minimizing total transportation cost into a constraint, resulting in obtaining pareto optimal solutions. This method makes up for the lack of existing research on the combination of both engineering logistics and multimodal transportation, after which the feasibility of the model and algorithm is verified by examples. The results show that the model solution with the introduction of the key processes at work zones produces more time-efficient and less time-consuming route planning results, and that the results obtained using the ε − constraint method are more reliable than the traditional methods for solving multi-objective planning problems and are more in line with the decision maker's needs. [ABSTRACT FROM AUTHOR]

While high-speed rail networks involve significant public expenditure in the name of the whole nation, scholars have paid little attention to the share of the population served by this modern transport mode. Studies have favoured perspectives that either neglect population volumes or include them but cannot offer a worldwide assessment. Public authorities favour passionate statements about network length and cruising speed but at the same time they neglect the populations served. In contrast, this paper offers a worldwide comparison of populations served by high-speed rail services in 15 countries. We found the range is wide (7%−94%) and depends on several factors beyond network length, including network design, urbanisation patterns, technical characteristics and domestic vs. international purposes. [ABSTRACT FROM AUTHOR]

Recently, new travel options such as micromobility and shared transport systems have been introduced in many urban areas in Spain, which are intended to increase the sustainability and accessibility of cities. The Covid-19 pandemic has boosted the use of these new individual travel modes. The aim of this paper is to analyze travel behavior and wellbeing of users of these new urban solutions compared to traditional transport. Face-to-face and online surveys were carried out during the Summer of 2021 in Valencia (Spain) to collect information regarding travel behavior and wellbeing. Participants were habitual users of all urban travel modes, including those who also used motosharing, carsharing or private e-scooters. 2225 valid responses were obtained after data cleaning and validation. Satisfaction with Travel Scale (STS) is used to explore subjective wellbeing of users of new and traditional transport modes. Results show that current users of motosharing and private e-scooters were former users of public transport, while users of carsharing used to utilize more car and motosharing before. On the other hand, active transport is associated with higher satisfaction compared to new urban modes, while they provide more satisfaction than urban public transport. [ABSTRACT FROM AUTHOR]

Multimodal transportation is a modern way of cargo transportation. With the increasing demand for cargo transportation, higher requirements are being placed on multimodal transportation multi-objective routing optimization. In multimodal transportation multi-objective routing optimization, in response to the limitations of classical algorithms in solving large-scale problems with multiple nodes and modes of transport, the limitations of directed transportation networks in the application, and the uncertainty of transport time, this paper proposes an optimization framework based on multi-objective weighted sum Q-learning, combined with the proposed undirected multiple-node network, and characterizes the uncertainty of time with a positively skewed distribution. The undirected multiple-node transportation network can better simulate cargo transportation and characterize transfer information, facilitate the modification of origin and destination, and avoid suboptimal solutions due to the manual setting of wrong route directions. The network is combined with weighted sum Q-learning to solve multimodal transportation multi-objective routing optimization problems faster and better. When modeling the uncertainty of transport time, a positively skewed distribution is used. The three objectives of transport cost, carbon emission cost, and transport time were studied and compared with PSO, GA, AFO, NSGA-II, and MOPSO. The experimental results show that compared with PSO, GA, and AFO using a directed transportation network, the proposed method has a significant improvement in optimization results and running time, and the running time is shortened by 26 times. The proposed method can better solve the boundary of the Pareto front and dominate the partial solutions of NSGA-II and MOPSO. The effect of time uncertainty on the performance of the algorithm is more significant in transport orders with high time weight. With the increase in uncertainty, the reliability of the route decreases. The effectiveness of the proposed method is verified. [ABSTRACT FROM AUTHOR]

Place-based accessibility measures communicate the potential interaction with opportunities at a zone that populations can access. Recent research has explored the implications of how opportunities are counted by different accessibility methods. In conventional measures, opportunities are multiply counted if more than one zone offers access to the same opportunity. This multi-count of opportunities leads to values of accessibility that are difficult to interpret. A possible solution to enhance the meaning-making of accessibility results is by constraining the calculations to match a known quantity. This ensures all zonal values sum up to a predetermined quantity (i.e., the total number of opportunities). In this way, each value can be meaningfully related to this total. A recent effort that implements this solution is spatial availability, a singly-constrained accessibility measure. In this paper, we extend spatial availability for use in the case of multiple modes or more generally, heterogeneous population segments with distinct travel behaviors. After deriving a multimodal version of spatial availability, we proceed to illustrate its features using a synthetic example. We then apply it to an empirical example of low emission zones in Madrid, Spain. We conclude with suggestions for future research and its use in evaluating policy interventions. [ABSTRACT FROM AUTHOR]

Wireless Power Transfer (WPT) technology has been developing rapidly in recent years. Electric Vehicles (EVs), as an emerging mode of transport, also benefit from the WPT technology. Various EV manufacturers have different coupling structures, which can lead to incompatibility problems between the coils. Meanwhile, due to the human parking, misalignment between the transmitting and receiving coils may occur. To solve these problems, this paper proposes a reconfigurable bipolar coil structure with both interoperability and misalignment tolerance. A 200-W experimental prototype is built, which can achieve 84.33% and 84.76% system transmission efficiency with vertical and horizontal bipolar coils. The effectiveness of the proposed reconfigurable bipolar coil is finally verified by comparing the computational and experimental results. [ABSTRACT FROM AUTHOR]

Despite many years of research and rapid changes in the field of bicycle route choice modelling, a review of factors associated with cyclists' route choice decisions is missing. This paper reviews 33 studies that employed GPS data and statistical modelling to explain the route choice behaviour of cyclists on utilitarian trips. Factors associated with bicycle route choice are divided into network, contextual, and individual factors. Findings from this review demonstrate that a refined categorisation of model attributes is essential, as the preferences vary greatly between different route attributes. Moreover, the analysis indicates a strong influence of the local context on the attribute definitions and route choice preferences. Although some commonalities between studies w.r.t. the network factors (e.g. a preference towards bicycle infrastructure or avoidance of slopes) can be determined, many results are contradictory or intertwined with other factors. Specifically, results related to intersections, land use characteristics, and interaction with other modes of transport are not always conclusive and are strongly dependent on the local context. However, the explanation of some of the unclear effects can be facilitated by incorporating contextual or individual information in the models. Based on the analysis of the existing studies, this review encourages a joint consideration of studies employing quantitative and qualitative methods to explain cyclists' behaviour. Moreover, reporting model results in an accessible form (e.g. through relatable values or visualisation) is essential to build bridges between bicycle research and policymaking. Future research is encouraged to study cyclists' route choice decisions from the perspective of objective well-being, quantifying and modelling the influence of factors such as safety, health, energy expenditure, or noise exposure. [ABSTRACT FROM AUTHOR]

Promoting the shift from private cars to micromobility (e.g., bike, e-bike, scooter) can represent a valuable action to improve city sustainability and liveability. Micromobility can help to replace trips by individual private cars (e.g., daily short round trips) as well as to improve coverage and accessibility of transit services, and, subsequently, to reduce the traffic impacts (e.g., pollutant emissions). It can be seen as a potential solution to move people more efficiently in urban areas, as well as to push people towards a more active mobility behaviour, contributing to the well-being goals. In this context, the paper, rather than inferring the users' propensity to change their travel mode, proposes a methodology to identify car trips that can be considered the most compatible with micromobility. Estimation of the potential demand (e.g., the upper level of car trips that could be replaced by micromobility) is carried out by exploiting the opportunity offered by floating car data (FCD) for characterising car trips. Its goodness is therefore evaluated through an application to a real case study (i.e., the city of Trani, Apulia Region, Southern Italy), divided into seventy traffic zones, and where a FCD dataset of about 5,200 trips was available. The FCD allowed the car trips to be characterised (e.g., origin and destination, path features) instead of using the traditional surveys. The results indicate that a significant share of daily car trips can be substituted (i.e., the most compatible) by micromobility (31% of car round trips in the case study), with considerable potential environmental gains (traffic emission reduction; less than 21% of total emissions from private cars). Results can be of interest to local authorities in integrating micromobility in urban mobility planning and promoting new sustainable transport alternatives, as well as to transport companies for designing new appeal services. The developed methodology is parametric and uses easy-to-obtain data available worldwide; thus, it can be easily transferred to other city contexts. [ABSTRACT FROM AUTHOR]

To promote the domestic economy and reduce transport costs, it is necessary for Chinese express companies to adopt the integrated air-rail freight transport mode. Thus, a major challenge is to determine how to efficiently use three kinds of transport vehicles: cargo aircraft, passenger aircraft bellies and high-speed train (HST) bellies. Therefore, in this paper, a bi-level model is constructed to allow an express company to optimise its integrated air-rail transport network and parcel delivery scheme. A hybrid algorithm is developed for solving the model. Data from the logistics company SF Express are used for a case study. From the calculated results, we find that the HST transport mode plays the main role, it can replace the passenger aircraft transportation to a large extent. Moreover, the total transport cost depends on the rental price of HST space largely. Furthermore, the timeliness requirement can largely influence the HST transportation ratio. [ABSTRACT FROM AUTHOR]

Compared with the traditional travel mode, the increasingly mature autonomous driving and shared travel technologies can lead to a higher driving utilization rate and lower car parc; however, at the same time, they will bring unknown impacts to the trans‐energy system. As a new generation of vehicles, the behavior of electric vehicles will also be affected. This paper describes and models the behavior of electric vehicle group in the autonomous driving‐shared travel mode in detail, and uses the multi‐agent technology to establish a large‐scale electric vehicle group simulation model. This model fully considers the constraints of traffic network and charging station and the influence of fuel vehicles, which can simulate the actual scene well and be used to study the charging behavior of electric vehicle group. Finally, the simulation model is used to obtain the charging load curve of the electric vehicle group under the new travel mode and analyze the influence of travel upgrade and network topology on charging load. [ABSTRACT FROM AUTHOR]

Purpose: This paper examines what facilitates the swift reconfiguration of freight movements across transport modes in the wake of a major disaster. Design/methodology/approach: A qualitative research approach focussing on the New Zealand (NZ) domestic freight transport operations in the wake of the 2016 Kaikōura earthquake is used with data collected through 19 interviews with 27 informants. The interviews are thematically analysed by using the framework method. Findings: The paper provides rich and detailed descriptions of the ability of a freight transport system to recover from a disaster through rapid modal shifts. This paper identifies nine factors enabling modular transport operations and highlights the critical role of physical, digital, operational and inter-organisational interconnectivity in the aftermath of a disaster. Originality/value: Although the management of freight disruptions has become a prevalent topic not only in industry and policy-making circles, but also in the academic literature, qualitative research focussing on the ability of commercial freight systems to adapt and recover from a disaster through rapid modal shifts is limited. This qualitative study sheds light on the mechanisms underlying the continuity of freight operations in the wake of a disaster and provides a comprehensive understanding of modular transport operations and the ability of freight systems to keep goods moving. [ABSTRACT FROM AUTHOR]

This paper evaluates a congestion management concept involving conversion of two existing general-purpose freeway lanes per direction on an 8-lane freeway (with 4 lanes per direction) to priced lanes offering free access for high-occupancy vehicles, in conjunction with cash payments to incentivize a sufficient number of commuters to travel as passengers in transit, carpools or vanpools such that congestion on the remaining free lanes would be no worse than it was before the conversion. Building on prior research, we use a refined modeling process to explore the viability of implementation of the concept on the eastern segment of the Capital Beltway in the Washington, DC metro area. The results of the analysis suggest that the strategy can benefit users of shared travel modes and toll-payers, improve travel speeds on toll-free general-purpose lanes, generate significant surplus revenues to support capital investments and operating subsidies for shared travel modes, and can absorb traffic growth through 2040. [ABSTRACT FROM AUTHOR]

The modern urban transportation service network could be split into unrestricted and restricted networks depending on whether travelers face limitations in route selection. Along with the continuous expansion of the city, it is difficult for travelers to find a more reasonable travel solution when confronted with such a complex transportation service network, which combines both unrestricted and restricted networks, especially for the park-and-ride (P&R) travel mode. This paper addresses the issue of route analysis in modern urban transportation service systems to provide travelers with reasonable travel solutions based on multiple types of transportation services. An improved A* algorithm is proposed to address the optimal path analysis for restricted networks to provide reasonable travel solutions for public transportation trips. Furthermore, by establishing the topological relationship between restricted and unrestricted networks, this paper presented an improved A* algorithm based on hybrid networks that solves the optimal path analysis problem for P&R trips, bringing convenience to many urban travelers. [ABSTRACT FROM AUTHOR]

This research paper analyzes urban travel mode preference in Addis Ababa using a survey distributed to 457 public transit users. The article highlights the details of individual travel and then discusses the implications of the travel patterns for transport policy in the city. The study employed percentages and run multinomial logistic regression to analyze the relationship between mode choice, travel characteristics and socio-economic features of individuals. In terms of mode choice and the influential factors, the analysis shows that income, age, travel expenditure and distance are the determining factors that affect mode choice. It was also found that the privately owned public transport, Taxi (mini-bus) and Higer (midi-bus) form the dominant mode of transport used by closely half percent (41.3%) of the survey respondents. On the basis of our analysis, we suggest that policies have to be devised to make government owned public transport service better in order to support a shift from using low occupancy vehicles to high occupancy vehicles which ultimately solve the problem of congestion and environmental pollution. [ABSTRACT FROM AUTHOR]

Demand-responsive feeder transit (DRFT) is an emerging urban public transport mode with the advantage of offering flexible door-to-door services in the high-speed railway hub area. However, the existing bus scheduling schemes can hardly meet personalized and diversified passenger transfer demands in the station-city integrated high-speed railway hub, reducing the attractiveness of DRFT. This paper studies the DRFT scheduling problem considering static and dynamic travel demands under the background of mobility as a service (MaaS). An information-based DRFT system framework is proposed, where the K-means clustering algorithm is implemented to select target bus stops from regional road networks for passengers to get on and off. A two-stage mixed integer programming model is first formulated to generate operational routes and optimize the static and dynamic scheduling before and after departure. The objective functions reflect the operating benefits of public transport enterprises and the travel costs of passengers, and the demand characteristics in different driving directions are taken into account in the model. Then, an improved genetic algorithm is developed to solve the model, which is called the genetic algorithm-exact algorithm (GA-EA) in this paper. Finally, the proposed model and algorithm are evaluated using the case study of the Nanjingnan Railway Station area. The experiment results show that the optimal scheme can provide a 100% demand-response rate, reasonable service time, and valid driving routes. In addition, compared with GA, the average search time of GA-EA is shortened by 43.5% and the total objective function value is increased by 2.16%. The findings in this paper can provide practical guidance on DRFT scheduling and improve the efficiency of bus feeder service. [ABSTRACT FROM AUTHOR]

Park-and-Ride (P&R), referring to the transfer between private cars and public transportation, is a public policy and travel mode that advocates green travel to reduce traffic congestion in big cities. Through an analysis on the development and focus of P&R research in China and abroad, this paper examines the research progress and explores the research trend on P&R from the perspective of travel mode choice, transfer demand forecast, location planning, operational management, and benefit evaluation. It points out that, compared with the research abroad, the P&R research in China is insufficient on the subjects of transportation planning and benefit evaluation. It proposes that, in future, research should focus more on planning, management, and benefit evaluation of P&R, so as to enhance the intelligent management of transportation system and promote the implementation of policies supporting P&R development. [ABSTRACT FROM AUTHOR]

The daily trips of urban residents in different transport modes reflect the urban mobility pattern and the latent spatial structure of the city, which is seldom addressed in the literature. A network-based cluster model is applied in this paper based on multimode transit big data. Thus, the urban spatial form can be observed at different levels according to the travel modes and distance, differing from traditional geographic structures. This paper will identify the urban mobility pattern and the corresponding spatial form by taking the Beijing metropolitan area as a study area. Origin–destination data at the traffic analysis zone level were collected, covering shared bike, bus, and metro. The results show that the metro trips reflect the single-center feature of Beijing. However, the multicenter development trend can be revealed by bus and shared-bike data. This paper finds that shared bike supports the establishment of subcenters as a new transport mode. The urban spatial form presents a complex feature of multiple levels and spatial diversity due to the comprehensive effect of multiple travel modes. The study results can provide a basis for the planning of transit network and urban equilibrium land use and development. [ABSTRACT FROM AUTHOR]

Cellular signaling data have become increasingly indispensable in analyzing residents' travel characteristic. Especially with the enhancement of positioning quality in 4G-LTE and 5G wireless communication systems, it is expected that the identification accuracy of fine-grained travel modes will achieve an optimal level. However, due to data privacy issues, the empirical evaluation of the performance of different identification methods is not yet sufficient. This paper builds a travel mode identification model that utilizes the gated recurrent unit (GRU) neural network. With 24 features as input, this method can identify four traffic modes, including walking, bicycle, car, and bus. Moreover, in cooperation with the operator, we organized an experiment collecting cellular signaling data, as well as the corresponding GPS data. Using the collected dataset as ground-truth data, the performance of the method presented in this paper and other popular methods is verified and compared. The results indicate that the GRU-based method has a better performance, with a precision, recall, and F score of 90.5%. Taking F score as an example, the outcome of the GRU-based method is about 6% to 7% higher than methods based on other machine learning algorithms. Considering the identification accuracy and model training time comprehensively, the method suggested in this paper outperforms the other three deep learning-based methods, namely, recurrent neural network (RNN), long short-term memory network (LSTM), and bidirectional long short-term memory network (Bi-LSTM). This study may provide some insights for the application and development of cellular signaling-based travel information collection technology for residents in the future. [ABSTRACT FROM AUTHOR]

Real‐time individuals' destination prediction is of great significance for real‐time user tracking, service recommendation and other related applications. Traditional technology mainly used statistical methods based on the travel patterns mined from personal history travel data. However, it is not clear how to predict the destinations of individuals with only limited personal historical data. In this paper, taking the public transportation metro systems as example, we design a practical method called practical model with strong interpretability and predictability to predict each passenger's destination. Our main novelties are two aspects: (1) We propose to predict individuals' destination by combining personal and crowd behavior under certain context. (2) An explanatory model combining discrete choice model and neural network model is proposed to predict individuals' stochastic trip's destination, which can be applied to other transportation analysis scenarios about individuals' choice behavior such as travel mode choice or route choice. We validate our method based on extensive experiments, using smart card data collected by automatic fare collection system and weather data in Shenzhen, China. The experimental results demonstrate that our approach can achieve better performance than other baselines in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

The use of smartphone tracking is seen as the way forward in data collection for travel behavior studies. It overcomes some of the weaknesses of the classical approach (which uses paper trip diaries) in terms of accuracy and user annoyance. This article evaluates if these benefits hold in the practical application of smartphone tracking and compares the findings of a travel behavior survey using smartphone tracking to the findings of a previous paper survey. We compare three phases of the travel behavior study. In the recruitment phase, we expect smartphone tracking to make people more willing to participate in surveys, given the innovative nature and reduced burden to participants. However, we found the recruitment of participants equally challenging as for classical methods. In the data collection phase, however, we observe that participants entering the smartphone tracking survey are much more likely to complete the data collection period than when using paper trip diaries. Because of the limited burden, the risk of drop-out from the survey is significantly lower, making the actual data collection more efficient, even for longer survey periods. Finally, in the data analysis phase, the travel behavior indicators derived from smartphone tracking data result in higher average trip rates, shorter average trip lengths and a higher share of active modes (bike, walking) than the results from the paper survey. Although this is explained by more complete and more consistent trip registration, this finding is problematic for comparability between surveys based on different methods, both for longitudinal monitoring (comparability over consequent surveys) and for benchmarking (comparability over geographical areas). Therefore, it is crucial to clearly report the applied data collection methods when describing or comparing travel indicators. In surveys, a combined approach of both written trip diaries and smartphone tracking is advised, where each method can complement the shortcomings of the other. [ABSTRACT FROM AUTHOR]

Many researchers have explored public transport assignment methodologies employing transport modelling software. Nevertheless, there remains a gap in evaluating real-world public transit networks utilizing diverse assignment procedures within Visum software. This paper introduces a thorough comparison of algorithms involved in public transport assignment processes, using the transport model of the city of Kryvyi Rih in Ukraine. The three scenarios of the model were developed depending on the public transport assignment procedure: headway-based, transport system-based utilized to all links, turns, and major turns in the network graph, and transport system-based applied only to the links, turns, and major turns traversed by the active public transport lines. The model of the network comprises 13 transport systems, 7 transport modes, 27598 links, 10097 nodes, 83270 turns, 238 zones, 1748 connections for private transport, 3013 connections for public transport, 534 stops, 1165 stop areas, 1190 stop points, 130 lines and 218 line routes. The transport demand model encompassed 14 demand segments. Compared to the outputs of the model calculation using the headwaybased procedure, in the scenarios with transport system-based assignment, passenger flows on rail tram lines significantly decreased. Also, the results of scenarios with transport system-based assignment showed that the passengers extremely use parallel streets for travel from an origin to a destination. The modelling outputs for an actual urban network illustrate that the choice of the shortest route in the transport system-based assignment is closely linked to the main urban roads. This circumstance could potentially result in a rise in the number of transfers needed to sustain accessibility to districts residing far from the central highway. [ABSTRACT FROM AUTHOR]

Air transportation is a mode of transport developed specifically to provide access to long-distance destinations that may otherwise be infeasible or at least more difficult to reach. Accordingly, there are distinct aspects of air travel that require consideration when assessing accessibility by air transportation, which are not addressed in general local accessibility studies. This paper reviews studies of accessibility by air transportation. The studies in question were published in academic journals, and we analysed them using the broad theoretical framework of accessibility of Geurs and van Wee [Accessibility evaluation of land-use and transport strategies: Review and research directions. Journal of Transport Geography, 12(2), 127–140]. We discuss the characteristics of accessibility by air transportation to consider when designing research, as well as trends and knowledge gaps in the existing literature. An important gap is that, while there is a growing interest towards perceived accessibility by air transportation, accessibility measures based on this perspective have not yet been applied yet. Additionally, the literature is largely focused on the transport and land-use components of accessibility as opposed to the individual and temporal components. We finish by discussing the benefits and drawbacks of directly applying conventional accessibility measures for research on accessibility by air transportation, as well as options to modify the temporal component of conventional person-based accessibility measures for better applicability in the air travel context. [ABSTRACT FROM AUTHOR]

Predicting and understanding travellers' mode choices is crucial to developing urban transportation systems and formulating traffic demand management strategies. Machine learning (ML) methods have been widely used as promising alternatives to traditional discrete choice models owing to their high prediction accuracy. However, a significant body of ML methods, especially the branch of neural networks, is constrained by overfitting and a lack of model interpretability. This study employs a neural network with feature selection for predicting travel mode choices and Shapley additive explanations (SHAP) analysis for model interpretation. A dataset collected in Chengdu, China was used for experimentation. The results reveal that the neural network achieves commendable prediction performance, with a 12% improvement over the traditional multinomial logit model. Also, feature selection using a combined result from two embedded methods can alleviate the overfitting tendency of the neural network, while establishing a more robust model against redundant or unnecessary variables. Additionally, the SHAP analysis identifies factors such as travel expenditure, age, driving experience, number of cars owned, individual monthly income, and trip purpose as significant features in our dataset. The heterogeneity of mode choice behaviour is significant among demographic groups, including different age, car ownership, and income levels. [ABSTRACT FROM AUTHOR]

Mobility-as-a-Service (MaaS) system is regarded as one of the emerging solutions to offer integrated, seamless, and flexible multi-modal mobility services as an alternative to privately owned mobility resources. MaaS is expected to change the way users will choose their modes of transport to reach their daily activities, and how service providers will generate profits, cooperate, and compete. To successfully deploy MaaS to reach the intended goals, it is critical to develop feasible and sustainable models that capture the diverse needs of customers as well as the diverse and often competing objectives of service providers. This paper aims to provide a general modelling framework and a critical and descriptive analysis of the relevant literature relating all main actors in the MaaS ecosystem, and identify and discuss all factors that are considered relevant, focusing on the actor's decision-making processes and their correlations. This review shows the large variety and interaction of factors influencing MaaS adoption and their impact on forecasting MaaS appeal. It is also observed that current travel behaviour and multi-modal transport models are not fully capturing the diverse travel needs and choices of potential MaaS users. Recent advancements in agent-based simulation and discrete choice modelling offer potential solutions to address this gap, and future research should aim in that direction. Finally, the review analyses the interaction between MaaS actors, including customers, service providers, the government, and the MaaS Broker, highlighting the complexity of the modelling process comprising all actors of the MaaS ecosystem. Therefore, it is recommended to prioritise future research in exploring these areas. [ABSTRACT FROM AUTHOR]

The current paper is based on data collected within the framework of the Inter-Connect PLUS project, with a focus on incorporating new norms before, during, and after the COVID-19 pandemic. A questionnaire was conducted in four countries of the Adriatic-Ionian (ADRION) Region: Italy, Slovenia, Croatia, and Albania. The collected data from the survey was analyzed to draw conclusions about the travel modes citizens used before, during, and are willing to use after the pandemic. The paper closes with recommendations, in the form of a Roadmap, highlighting the potential positive impact of the pandemic on the future of sustainable mobility. [ABSTRACT FROM AUTHOR]

Aim: Active transportation referring to non-motorized modes of transport is promoted and popularized both in practice and in the scientific literature, while their use for urban freight transport has been largely neglected. Thus the main scope of the paper is to indicate the development potential of micromobility use in urban freight transport and to check its influence on urban sustainability. Methods: The authors have hypothesized that active means of transport, with a focus on micromobility, have great development potential in freight transportation in cities. The implemented methods for analyzing the relationship between users' characteristics, micromobility, and its impact on urban sustainable development, were logit and probit modelling. The authors' system includes an analysis of factors connected with the topics of sustainability and micromobilty, that have met an essential scientific gap that this paper addresses. Logistic (logit) regression is used mainly for binary, ordinal, and multi-level outcomes to find the probability of success (i.e. occurrence of some event). Probit regression, however, is primarily used in binary response models and assumes the normal distribution of data. Results: The main finding of the article has led the authors to the statement that active means of transport, including micromobility have great development potential in freight transportation in cities. Conclusions: Knowledge of the acceptance of micromobility solutions is essential for municipal authorities in shaping the development of urban transport systems. Thus proper strategies and actions need to be prioritized to leverage the sustainability-related co-benefits of active transport. [ABSTRACT FROM AUTHOR]

After entering the 21st century, China's national economy has shown a rapid growth momentum, the comprehensive transportation system has been continuously improved, the road traffic infrastructure has made remarkable achievements, and the modern logistics industry has also risen rapidly and grown rapidly, which has greatly changed the market demand for road transport hubs. The road transport hub is the main node of the road transport network, the hub of passenger and freight distribution of road transport, and the organizational center for the interconnection of road transport and other transport modes and the development of comprehensive transport. Highway transportation hub is an important part of highway transportation infrastructure and plays an important role in highway transportation. The planning scheme evaluation of highway transportation hub is a multi-attribute decision making (MADM). This paper intends to propose a MADM methodology based on cross-entropy (CE) method under interval-valued intuitionistic fuzzy sets (IVIFSs) for planning scheme evaluation of highway transportation hub. First of all, this paper extends the cross entropy method under the IVIFSs to propose the interval-valued intuitionistic fuzzy number CE(IVIFN-CE) method, it enlarges the application range of the CE method. Secondly, a new MADM model for planning scheme evaluation of highway transportation hub based on IVIFN-CE algorithm is proposed. [ABSTRACT FROM AUTHOR]

A range of studies have found that immigrants generally start out using different travel modes but over time they 'assimilate' toward adopting similar travel modes to the general population. These studies tend to focus on 'when' and 'if' travel assimilation occurs, with some studies using socioeconomic factors to explain 'why' this occurs. But few studies have explored the role of culture, attitudes and other 'soft' factors in shaping the process of travel assimilation among immigrants. In Australia, South Asians have been the largest and fastest growing immigrant group, and as skilled migrants they face few 'hard' barriers to car use. The aim of this paper is to explore the interaction between cultural influences, attitudes and initial travel experiences upon arrival in Australia on long-term travel assimilation amongst South Asian immigrants. Qualitative interviews with 20 South Asian immigrants were used to identify a range of cultural and psychosocial factors, such as perceptions towards travel modes and gender-based cultural norms. Attitudes and behaviours evolve during their early years in Australia, beginning with a 'honeymoon period' – a phase where all travel modes are seen as positive – before car use begins to dominate. The findings have implications for how we understand the interactions between attitudes, cultural practice and travel behaviour and how they evolve over time. They also imply that policymakers have only a narrow window of time to encourage sustainable transport among South Asian immigrants before the travel 'honeymoon period' wears off. [ABSTRACT FROM AUTHOR]

A metropolitan area is a new form of urban development under the agglomeration effect and scale economy. The renewal and upgrading of urban spatial structures have brought new pressure to urban commuting. Under the new form of the metropolitan area, the process of regional integration has accelerated, and long-distance extreme commuting has increased. New changes have taken place in the travel structure. This paper constructs a travel behavior selection model for office workers based on the cumulative prospect theory, introduces the value of commuting travel time into the generalized travel cost function, uses the weight function and the improved generalized travel cost function as the basis of the transportation mode selection model, defines the reference point of the generalized travel cost in the model, and selects the prospect with the largest cumulative prospect value as the optimal decision for travelers. Based on the "expected utility maximization theory" and the "cumulative prospect theory," the commuter is simulated under four different travel scenarios to study the optimal traffic mode selection behavior. The results show that under the framework of expected utility theory, the travel mode choice behavior of commuters is not affected by travel scenarios, and the cumulative prospect theory is more suitable for the study of travel mode choice behavior. The construction of a transportation mode selection model with the value of commuting travel time as the core variable can help commuters to choose a reasonable transportation mode and provide a basis for the government and relevant departments to formulate traffic management plans and implement traffic congestion mitigation policies. [ABSTRACT FROM AUTHOR]

The sharing economy, mobile Internet, and smartphones have been widely utilized in recent years to promote the development of bike-sharing services. Bike-sharing serves as a first/last mile travel mode to connect to public transit, which improves trip efficiency, alleviates traffic problems, improves environmental quality, and promotes public health. However, the substitution of public transit by bike-sharing and the decline in public transit ridership have raised concerns among city managers regarding the coopetition between shared mobility services and public transit. To understand the impact of bike-sharing on the decline in public transit and to formulate reasonable synergistic development policies, it is crucial to identify the coopetition relationships between the two. This paper uses a combination of database search and backward snowballing to review existing research. Three research themes were identified: macrolevel studies on bike-sharing and public transit interaction, studies on actual coopetition behaviors based on bike-sharing user surveys, and studies on potential coopetition relationships based on bike-sharing transaction data. The three categories of studies reveal the effect of bike-sharing usage on public transit ridership, the emergency function of bike-sharing in the event of unexpected transit shutdowns, and the substitution and connection relationships between bike-sharing and public transit and the factors influencing them. Finally, this study suggests many directions for future research. This review helps clarify the understanding of the coopetition relationships between bike-sharing and public transit, provides theoretical support to promote the synergistic development of both, and points out ways to deepen the research on the coopetition relationship between the two. [ABSTRACT FROM AUTHOR]

From a socialist society where public transit was the primary form of transportation to the modern “Silicon Valley” of Eastern Europe, where the streets are flooded with cars. Cluj has changed over the years; overturned by university students for most of the year, the city and encompassing communes have experienced significant growth, which stresses the existing socialist infrastructure and causes high levels of strategic planning to accommodate the present and future transportation plans. Moreover, the CTP is no exception; as the only provider of public transportation in Cluj, it is at the centrefold of this predicament and needs to distinguish itself as a success within Cluj to provide for the community's needs. In this paper, we look at data from multiple sources to analyze what drives people's choice of transportation and pinpoint some fundamental notions as to why people choose private vehicles and other forms of transportation over public transit. Then, using SWOT and PEST analysis, we look to find solutions to benefit the community. Many options are available to develop more fluidity in the city, change the notion of public transportation, and create reliance on public transportation. [ABSTRACT FROM AUTHOR]

This paper focuses on modeling transportation mode choice for commuting to work and school. The study employs a Multinomial Logit (MNL) model to examine how individuals choose their modes of transportation for work and school trips in Hawassa city. Additionally, the study aims to predict both the current and future distribution of transportation modes. To construct the model, surveys were distributed across the city's seven sub-cities, involving inspections of workplace and school travel. Primary data were collected through site visits to key transportation hubs, and travel cost data were obtained from the city's Road and Transportation Bureau. The data used for the MNL model describe the travel behaviors of employees and students, and these behaviors are integrated into the statistical analysis to formulate utility functions. The choice of transportation mode for a trip is treated as the dependent variable, while independent variables include factors like out-of-vehicle travel time, in-vehicle travel time, and travel cost. The model's validity and accuracy were assessed by examining the direction of parameter signs and comparing them to the fundamental properties of the MNL model. The study revealed that factors such as average monthly income, in-vehicle travel time, out-of-vehicle travel time, total travel cost, and comfort during the journey significantly influence individuals' choices of transportation modes. The results from the travel behavior forecast, which examines how employees choose their transportation modes, highlight the pressing need for implementing effective policy measures to incentivize the adoption of more sustainable transportation modes and promote a modal shift. Without such measures, employees are likely to increasingly favor motorcycles as their preferred mode of transportation, potentially exacerbating issues related to fuel consumption and congestion. It's evident that students tend to favor public transportation over motorcycles when selecting their mode of travel. The findings of this study offer valuable insights to decision-makers and transportation planners, shedding light on the critical factors influencing travel patterns, as well as providing estimates of existing and future market shares. These findings can serve as a foundation for crafting targeted policy adjustments to encourage sustainable transportation choices in a comprehensive manner. [ABSTRACT FROM AUTHOR]

Public transport in Malaysia has grown through government investment aimed at alleviating traffic congestion in urban areas. However, the safety of women using public transport is often viewed as less significant and is generally treated as a universal design issue. Ironically, past research strongly indicates that women face safety and security concerns in public spaces and while using public transportation services, traveling differently from men in terms of mobility and accessibility. These gendered and socio-economic inequalities affect women's choice of transportation mode, negatively impacting their overall health, safety, and well-being. These elements must be planned thoroughly to achieve inclusive planning and gender equality in transportation. The main challenges to women's mobility in Malaysia include gender-based violence and issues related to the safety and access to public transportation. This paper is based on a Focus Group Discussion (FGD) regarding public transport safety attitudes and perceptions among female users aged 15 to 70 in Malaysia, focusing on variables such as safety, women-friendly facilities, and expectations vs. perceptions. The findings highlighted gender-based issues such as sexual assault, harassment, and child trafficking as major concerns impeding women's access to public transportation services in Malaysia. Participation by stakeholders in the provision of a holistic and inclusive public transport infrastructure remains a bureaucratic obstacle in Malaysia's planning and implementation of public transport services. Public transportation strategies need to be holistic, ensuring that the physical design and other strategies employed prevent security breaches and provide a sense of safety, especially for women and children. Post-incident support by designated and relevant agencies is also crucial. Infrastructure improvements need to be accompanied by awareness campaigns and advocacy to cultivate a more secure environment for everyone in public spaces. [ABSTRACT FROM AUTHOR]

Shared bicycles provide a green, environmentally friendly, and healthy mode of transportation that effectively addresses the "final mile" problem in urban travel. However, the uneven distribution of bicycles and the imbalance of user demand can significantly impact user experience and bicycle usage efficiency, which makes it necessary to predict bicycle demand. In this paper, we propose a novel shared-bicycle demand prediction method based on station clustering. First, to address the challenge of capturing patterns in station-level bicycle demand, which exhibits significant fluctuations, we employ a clustering method that combines graph information from the bicycle transfer graph and potential energy. This method aggregates closely related stations into corresponding prediction regions. Second, we use the GCN-CRU-AM (Graph Convolutional Network-Gated Recurrent Unit-Attention Mechanism) model to predict bicycle demand in each region. This model extracts the spatial information and correlation between regions, integrates time feature data and local weather data, and assigns weights to the input features. Finally, experimental results based on the data from Citi Bike System in New York City demonstrate that the proposed model achieves a more accurate demand prediction. [ABSTRACT FROM AUTHOR]

The i-DREAMS project established a 'Safety Tolerance Zone (STZ)' to maintain operators within safe boundaries through real-time and post-trip interventions, based on the crucial role of the human element in driving behavior. This paper aims to model the inter-relationship among driving task complexity, operator and vehicle coping capacity, and crash risk. Towards that aim, data from 80 drivers, who participated in a naturalistic driving experiment carried out in three countries (i.e., Belgium, Germany, and Portugal), resulting in a dataset of approximately 19,000 trips were collected and analyzed. The exploratory analysis included the development of Generalized Linear Models (GLMs) and the choice of the most appropriate variables associated with the latent variables "task complexity" and "coping capacity" that are to be estimated from the various indicators. In addition, Structural Equation Models (SEMs) were used to explore how the model variables were interrelated, allowing for both direct and indirect relationships to be modeled. Comparisons on the performance of such models, as well as a discussion on behaviors and driving patterns across different countries and transport modes, were also provided. The findings revealed a positive relationship between task complexity and coping capacity, indicating that as the difficulty of the driving task increased, the driver's coping capacity increased accordingly, (i.e., higher ability to manage and adapt to the challenges posed by more complex tasks). The integrated treatment of task complexity, coping capacity, and risk can improve the behavior and safety of all travelers, through the unobtrusive and seamless monitoring of behavior. Thus, authorities should utilize a data system oriented towards collecting key driving insights on population level to plan mobility and safety interventions, develop incentives for road users, optimize enforcement, and enhance community building for safe traveling. [ABSTRACT FROM AUTHOR]

Walking is a fundamental mode of transport for many people globally, with immense health and environmental benefits. However, increased walking is associated with an increased risk of road traffic crashes and injuries, especially where traffic enforcement is poor, and pedestrians could easily be distracted. Despite this, a plethora of evidence exists on driver distraction; however, there is a dread of evidence of pedestrian distraction (i.e. distracted walking), particularly in Africa. This paper uses a quantitative methodology to examine the predictors of distractive activities to walking in Accra, Ghana. The study surveyed 400 pedestrians within Accra's Central Business District (CBD). A questionnaire was deployed via Kobo Toolkit and Jamovi was used in analysing the data. The study observed that three of the top four distractive activities to walking were digital devices-related (e.g. the use of mobile phones). We found that listening to music, making, receiving phone calls, and conversing with other people while walking are the main distractive activities. The binary logistic regression model found sex, age, level of education, occupation, reasons for walking, weekly time for walking and time for common trips as significant predictors of distractive activities to walking. [ABSTRACT FROM AUTHOR]

Social and physical infrastructure development needs to catch up to population growth in cities of the Global South. The infrastructures are emerging in piecemeal ways following demand, be it housing, water supply, electricity, financial, educational, health or transport services. Indian cities are typical examples of this and can be called archetypes of the problem. This paper finds that transport infrastructure development in Mumbai Metropolitan Region has not kept pace with the demand. Notwithstanding recent developments, urban mobility in the region is characterised by traffic jams, delays and a lack of integrated and inclusionary transport systems, adversely affecting the economic efficiency of the region through loss of labour time, delays in deliveries of goods and services, environmental pollution and health costs. [ABSTRACT FROM AUTHOR]