Transportation Planning Research Articles

Smarter but more unequal transport? How socioeconomic and digital inequalities hinder adoption of mobility apps in the Global South

All over the world, cities are becoming “smarter.” The improved use of data generated by the transport system (e.g. smart cards data), combined with GPS trackers and citizens’ mobile data enabled the development of a new generation of smarter mobile apps (e.g., Waze) and several local real-time public transport services and trip planners. These technologies have the potential to reduce transport inequalities via two mechanisms. First, governments can develop more efficient and evidence-based public transport plans. Second, citizens have access to better, real-time information about public transport for trip planning and, thus, can make better—i.e., more efficient or cheaper—decisions regarding their everyday mobility. To assess whether citizens’ appropriation of smart mobility—as with their appropriation of any other digital service—depends strongly on having a certain set of socioeconomic and digital attributes, we analyzed both a mobility survey and a digital inequalities survey based on representative samples of adult Uruguayans living in the capital Montevideo. We find that while the use of mobility apps increased in the second half of the 2010s, the adoption of these tools was stratified by traditional digital inequalities, such as type of internet access and digital literacy, and by variables that predict inequalities in both digital and transport fields, such as age, gender and education. As a result, mobility continues to be stratified in favor of those who are more knowledgeable (i.e., tech-savvy) and have greater access to digital technology. Thus, “smart mobility” has failed to ameliorate—and may even exacerbate—transport inequality. Policy implications of these findings are discussed.

‘There is a lot of traffic at the school gate in the morning which makes me nervous’: exploring children’s current and desired school commutes

ABSTRACT Although children are the primary users of many spaces, their voices are often unheard in transport planning. We therefore do not know much about children’s barriers or wishes regarding mobility. What is known, however, is that children have become less independent in their daily mobility, with parents (or guardians) increasingly driving children instead of allowing them to walk and cycle. The decline in active mobility is worrisome, since active mobility is beneficial to children, but incorporating children’s voices in transport planning can be conducive to increased active mobility of children. The objective of this paper is to explore the wishes and barriers of children regarding school commuting. Data was collected from a survey completed by 1251 pupils and through workshops with four schools in a residential neighbourhood in Brussels, Belgium. We found that there is a significant gap between the current and desired transport modes of pupils, with an important untapped potential for cycling. We also found that traffic safety is a predominant worry of pupils. Additionally, when suggesting the re-design of their school neighbourhood, the most recurring suggestions from pupils included walking and cycling infrastructure, and reduced access to the school neighbourhood for cars.

Extending quantum detailed balance through optimal transport

We develop a general approach to setting up and studying classes of quantum dynamical systems close to and structurally similar to systems having specified properties, in particular detailed balance. This is done in terms of transport plans and Wasserstein distances between systems on possibly different observable algebras.

Spatial factors associated with usage of different on-demand elements within mobility hubs: a systematic literature review

ABSTRACT Mobility hubs offer a strategic opportunity to enhance multimodal transportation and reduce greenhouse gas emissions within the transport sector. Identifying the spatial factors determinants influencing the usage of mobility hub components is essential for policymakers and transport planners aiming to identify their optimal locations. This study undertakes a systematic literature review and ranking of spatial factors associated with the usage of key on-demand services within mobility hub elements, including bike sharing, scooter sharing, car sharing, ride hailing and taxi and electric vehicle charging stations. Utilising databases such as Web of Science, Google Scholar and Francis & Taylor, we evaluated 119 records, identifying 39 key factors from social and built environments that are associated with usage of the on-demand elements. Key findings highlight the significance of population density, employment density, proximity to public transport, recreational POIs and household income, along with a noted negative association with the factor slope. This research contributes to exploring how these factors align across various on-demand mobility services that can be potentially used for policymakers, and transportation planners in evaluating potential strategies for the optimal allocation and development of mobility hubs.

Implementation of smart mobility and relationship with transportation planning and regional development: A case study of South Tangerang city

Urbanization and suburbanization have led to high population growth in certain city regions, resulting in increased population density and mobility. Therefore, there is a need for a concept to address congestion, public transportation, information and communication systems, and non-motorized vehicles. Smart mobility is a concept of urban development as part of the smart city concept based on information and communication technology. Through this concept, it is expected that transportation services will be easily accessible, safe, comfortable, fast, and affordable for the community. This research aims to analyze smart mobility and its relationship with regional transportation planning and the development of South Tangerang, as well as to design a policy strategy model for the planning and development of South Tangerang with smart mobility. The research method used in this study is a mixed method, including analyzing the relationships and weighting of relationships between variables using the Cross Impact Multiplication applied to a classification (MICMAC) matrix. Multi-criteria decision analysis (MCDA) with Promethee software is also used to obtain the necessary policies. The results of this research indicate that the measurement of relationships between variables shows that smart mobility influences regional transportation planning, smart mobility affects regional development, and regional planning affects regional development. This research also provides alternative policies that policymakers should implement in a specific order. First, ensure the availability of public transportation; second, improve public transportation safety; third, enhance public transportation security; fourth, improve public transportation routes; fifth, provide real-time information access; sixth, improve transportation schedules; and seventh, increase the number of bicycle lanes.

Potentials of digital twin system for analyzing travel behavior decisions

This review explores the potential of digital twin systems to provide a more holistic representation of travel behavior and support transportation planning and policymaking. The paper introduces the concept of digital twins, their key characteristics, and their applications in various domains, including transportation. It discusses the traditional methods used in travel behavior analysis and their limitations, as well as the potential advantages of digital twin systems, such as the integration of heterogeneous data sources, real-time monitoring and prediction, and the ability to simulate and evaluate various policy scenarios. The review also identifies the key components of digital twin systems, the challenges associated with their implementation, and the current state of research on digital twins and related methods in travel behavior analysis. The paper highlights research gaps and future directions, emphasizing the need for privacy-preserving techniques, real-world case studies, and the integration of digital twins with decision support systems. Finally, the review discusses the broader implications of digital twin systems for transportation planning and policymaking, concluding by emphasizing the need for interdisciplinary collaboration and stakeholder engagement to fully realize the potential of digital twins in analyzing travel behavior decisions and shaping the future of transportation systems.

The deeper and wider social impacts of transportation infrastructure: From travel experience to sense of place and academic performance

Traditional transportation planning emphasized the direct economic benefits of transportation infrastructure. While there is a growing awareness of the environmental consequences, social impacts are frequently overlooked due to methodological complexities and political neglect. Although existing literature acknowledges the existence of transportation-related social exclusion, it primarily concentrates on quantifiable and immediate short-term social outcomes arising from improved accessibility and affordability, which facilitate place-to-place interactions and activities. There has been limited exploration of the more intricate and indirect societal impacts associated with transportation. This paper argues that new transportation infrastructure not only affects mobility and travel experiences but also has deeper social impacts on the sense of place. These effects may extend to broader benefits, including academic performance, by influencing place identity, attachment, and dependency, which in turn affect daily activities unrelated to travel. Our study focuses on a university student community (N = 323), a pivotal future workforce, and investigates how the introduction of a new metro line passing through the campus reduces commuting time, subsequently enhancing academic productivity. Employing structural equation modeling method, our analysis reveals that sense of place acts as a mediator between travel experience and student engagement under the influence of transportation mode choice. These findings may have broad applicability in identifying indirect social impacts of transportation infrastructure, thus providing insights for investment decisions.

Assessment and Modelling of Walk Trips in Akure, Nigeria

The study examined and modeled the walking behavior patterns of residents in Akure, Nigeria. A multi-stage sampling approach was used to collect relevant data for assessing and modeling the walking trip patterns. The questionnaires were administered to 300 participants between May to November 2019 (6 months) across three primary residential neighborhoods in the city; high-density, medium-density, and low-density areas. The obtained data were analyzed using SPSS software and subsequently modeled using multi-variable regression techniques. The study found that 48.7% of the sampled population preferred to walk, and 49.7% perceived walking as an alternative to motorized trips. Significant differences were observed in the socioeconomic characteristics of residents, such as age and income, across the residential zones (F = 54.731, p<0.001; F = 68.278, p<0.001). The key factors that greatly influenced decision of respondents to walk included socioeconomic factors, such as income, personal car ownership, educational qualifications, and employment status, with age and sex being the least significant socioeconomic determinants. Generally, the factors that motivated respondents to choose walking as a travel mode included its relative affordability, the lack of personal vehicles, awareness of health benefits, safety and security concerns, avoidance of motorized traffic congestion, and favorable weather conditions. The findings of this study have important implications for transportation planning. The paper recommends the need to incorporate non-motorized spaces and walkways into various levels of urban planning and implementation to foster a more walkable environment and encourage active transportation in the city.

Flood risk awareness and transportation mode choice in communities with varying flooding frequencies: The cases of Bozkurt, Kastamonu, Turkey and Malanday, Marikina, Philippines

This study investigates the post-flood transportation mode choices (TMC) of communities exposed to floods of varying frequencies, focusing on Bozkurt, Turkey, recently afflicted by a major flood, and Malanday, Philippines, which endures an average of 10 floods annually. The study considers 17 factors affecting TMC, including age, occupation, income, housing type, flood experience, education level, changes in travel time, primary transportation mode, duration of residence, gender, marital status, relocation status, personal losses, disruption of daily activities, time to return to routine, satisfaction with road maintenance, and satisfaction with flood measures. Data were collected through surveys and analysed using regression analysis.The findings indicate that personal and cultural characteristics, and flood frequency, have limited effects on TMC. The most impactful factors on post-flood TMC are changes in travel time (42.11 %), personal losses (23.75 %), and satisfaction with road maintenance (11.30 %). The study demonstrates that flood risk awareness is directly related to education level and flood experience. In Malanday, where 84 % of the population are university or high school graduates, floods are regarded as routine events. In contrast, in Bozkurt, where only 37 % have similar education levels, the community experienced panic under flood conditions, adversely affecting decision-making processes.The research provides insights into how communities exposed to floods adjust their TMC, offering essential information for mitigating flood risks and increasing flood awareness. Moreover, the findings deliver suggestive information for local governments and planners in developing more effective flood management and transportation planning strategies.

Access to green and gray urban nature amenities: exploring equity in Montreal's built environment

ABSTRACT Contemporary ideologies have identified two main paradigms contributing to sustainable cities: green urban and gray urban nature. The first refers to features that make cities greener, such as parks or tree coverage. The second refers to gray but sustainable areas, such as dense areas or the proximity to key amenities that can foster active travel. Accessibility to green and gray urban nature amenities by active transport is essential for evaluating if cities are sustainable. We analyzed the accessibility to green (green spaces) and gray (food stores and pharmacies) urban nature in Montreal. The findings show that most people have sufficient access to green and gray urban nature, and low-income groups have better access than high-income groups. Areas with access to both tend to be highly dense, suggesting that dense areas have sustainable attributes. Transport planning interventions in areas lacking access are necessary to achieve sustainable goals in Montreal.

Maximizers of nonlocal interactions of Wasserstein Type

We characterize the maximizers of a functional that involves the minimization of the Wasserstein distance between sets of equal volume. We prove that balls are the only maximizers by combining a symmetrization-by-reflection technique with the uniqueness of optimal transport plans. Further, in one dimension, we provide a sharp quantitative refinement of this maximality result.

Alignment and comparison of directed networks via transition couplings of random walks

Abstract We describe and study a transport-based procedure called network optimal transition coupling (NetOTC) for the comparison and alignment of two networks. The networks of interest may be directed or undirected, weighted or unweighted, and may have distinct vertex sets of different sizes. Given two networks and a cost function relating their vertices, NetOTC finds a transition coupling of their associated random walks having minimum expected cost. The minimizing cost quantifies the difference between the networks, while the optimal transport plan itself provides alignments of both the vertices and the edges of the two networks. Coupling of the full random walks, rather than their marginal distributions, ensures that NetOTC captures local and global information about the networks and preserves edges. NetOTC has no free parameters and does not rely on randomization. We investigate a number of theoretical properties of NetOTC and present experiments establishing its empirical performance.

Integrated optimization of logistics routing problem considering chance preference

PurposeThis study aims to examine the impact of the decision makers’ risk preference on logistics routing problem, contributing to logistics behavior analysis and route integration optimization under uncertain environment. Due to the unexpected events and complex environment in modern logistics operations, the logistics process is full of uncertainty. Based on the chance function of satisfying the transportation time and cost requirements, this paper focuses on the fourth party logistics routing integrated optimization problem considering the chance preference of decision makers from the perspective of satisfaction.Design/methodology/approachThis study used the quantitative method to investigate the relationship between route decision making and human behavior. The cumulative prospect theory is used to describe the loss, gain and utility function based on confidence levels. A mathematical model and an improved ant colony algorithm are employed to solve the problems. Numerical examples show the effectiveness of the proposed model and algorithm.FindingsThe study’s findings reveal that the dual-population improvement strategy enhances the algorithm’s global search capability and the improved algorithm can solve the risk model quickly, verifying the effectiveness of the improvement method. Moreover, the decision-maker is more sensitive to losses, and the utility obtained when considering decision-makers' risk attitudes is greater than that obtained when the decision-maker exhibits risk neutrality.Practical implicationsIn an uncertain environment, the logistics decision maker’s risk preference directly affects decision making. Different parameter combinations in the proposed model could be set for decision-makers with different risk attitudes to fit their needs more accurately. This could help managers design effective transportation plans and improve service levels. In addition, the improved algorithm can solve the proposed problem quickly, stably and effectively, so as to help the decision maker to make the logistics path decision quickly according to the required confidence level.Originality/valueConsidering the uncertainty in logistics and the risk behavior of decision makers, this paper studies integrated routing problem from the perspective of opportunity preference. Based on the chance function of satisfying the transportation time and cost requirements, a fourth party logistics routing integrated optimization problem model considering the chance preference of decision makers is established. According to the characteristics of the problem, an improved dual-population ant colony algorithm is designed to solve the proposed model. Numerical examples show the effectiveness the proposed methods.

Minimizing delays of patient transports with incomplete information: A modeling approach based on the vehicle routing problem

AbstractWe investigate a challenging task in ambulatory care, the minimizing of delays of patient transports. In practice, a limited number of vehicles is available for non-rescue transports. Furthermore, the dispatcher rarely has access to complete information when establishing a transport plan for dispatching the vehicles. If additional transport is requested on demand then schedules need to be updated, which can lead to long delays. We model the scheduling of patient transports as a vehicle routing problem with general time windows and solve it as a mixed-integer linear problem that is modified whenever additional transport information becomes available. We propose a modeling approach that is designed to determine fair and stable plans. Furthermore, we show that the model can easily be modified when transports need to satisfy additional requirements, e.g., during pandemics, exemplarily the Covid-19 pandemic. To show the applicability and efficiency of our modeling approach, we conduct a numerical study using historical data from the region of Middle Franconia. The results reveal and show that, by applying mathematical optimization—or, to be more precise by solving mixed-integer linear problem formulations—one can significantly decrease delays and have considerable potential for optimized patient transports.

Investigating the relative accuracy of GPS, GSM and CDR data for inferring spatiotemporal travel trajectories

AbstractThe potential of passively generated big data sources in transport modelling is well‐recognised. However, assessing their accuracy and suitability for policymaking remains challenging due to the lack of ground‐truth (GT) data for validation. This study evaluates the accuracy of inferring human mobility patterns from global positioning system (GPS), call detail records (CDR), and global system for mobile communication (GSM) data. Using outputs from an agent‐based simulation platform (MATSim) as ‘synthetic GT’ (SGT), synthetic GPS, CDR, and GSM data were generated, considering their positional disturbances and conventional spatiotemporal resolutions. Mobility information, including activity location, departure time, and trajectory distance, derived from the synthetic data, was compared with SGT to evaluate the accuracy of passive trajectory data at both disaggregate and aggregate levels. The results indicated a higher accuracy of GPS data in identifying stay locations at high resolution. But, GSM data at a lower resolution effectively accounted for over 80% of the variability in stay locations. Comparisons of departure time distribution and travel distance revealed higher measurement errors in GSM and CDR data than in GPS data. The proposed simulation‐based accuracy assessment framework will aid transport planners select the most suitable data for specific analyses and understand the potential margin of error involved.

Minimal‐cost route planning via Fibonacci‐heap‐typed data structure

AbstractEfficient route planning in transportation networks, particularly under stochastic conditions like severe weather (i.e. snow or hail), poses a significant computational challenge. This article addresses this challenge by modeling the route planning problem as a Markov decision process (MDP) problem, establishing reachability criteria, and identifying the minimum‐weight arborescence in the directed graph. To achieve this, the reachability determination algorithm is designed to assess the courier company's reachability to all junctions based on the queue‐typed data structure and breadth‐first search idea. Subsequently, the minimal‐cost route planning algorithm is developed to find a feasible transport route with the minimal cost of clearing obstacles by resorting to the Edmonds' algorithm and some feasible data structures. In particular, the article introduces a Fibonacci‐heap‐typed data structure to the minimal‐cost route planning algorithm, resulting in a remarkable reduction of the time complexity from to , where and represent the cardinalities of the arc set and nodeset, respectively. Ultimately, the proposed method is applied to optimize route planning in a transportation network, providing a cost‐efficient solution for logistics and transportation planning.

9 to 5 or a new‐normal? Cluster analysis of pre and post pandemic vehicle and cycle diurnal flow profiles

AbstractCommuting traffic associated with the “9 to 5” workday shaped the morning and evening peaks across the world. The COVID‐19 pandemic led to unprecedented changes in travel behaviour such as an increase in cyclists and telecommuting, where employees worked from home during lockdown periods. Transport modellers, planners and policy makers need to know whether the 9 to 5 has returned, or we have entered a “New‐normal” of more flexible working arrangements and increased cycling, key for delivering sustainability targets. In this research, the unsupervised machine learning technique k‐means clustering investigates temporal patterns across the day and week, comparing the pre‐ and post‐pandemic era across both motorised vehicles and bicycles. Results show that the total daily traffic flow has returned to pre‐pandemic volumes, but more spread across the day. Mondays and Fridays have less‐pronounced peaks compared to pre‐pandemic, having implications for air quality modelling and assessment, traffic management and transport planning. Meanwhile, cycling has increased in volume and the time‐of‐day people are travelling has changed. Policy makers need to consider whether the additional capacity on the road, brought about by reduced peak traffic, could be reallocated to make roads safer for and reduce delay to cyclists, contributing towards net zero goals.

Assessing Heterogeneity Among Cyclists Towards Importance of Bicycle Infrastructural Elements in Urban Areas

Promoting bicycling and making it attractive requires appropriate infrastructure. Sociodemographic characteristics, frequency and experiences of bike use, and purpose of bicycle trips can affect preferences towards bicycle infrastructure facilities in urban areas. Hence, this study aims to explore the heterogeneity in the perceived importance of bicycle infrastructure facility attributes in various cyclist groups based on gender, age, weekly biking frequency, daily cycling distance, cycling experience, and bicycle trip purpose. Data were collected from bicycle users through a questionnaire disseminated via social media platforms and QR code brochures distributed in Hasselt, Belgium. A 5-point Likert-type ordinal scale was used to collect data on the perceived importance of bicycle infrastructure facility indicators. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank the indicators. At the same time, Mann–Whitney U and Kruskal–Wallis tests were utilized to verify the heterogeneity among the groups. The findings reveal that bicycle infrastructure, i.e., bicycle lanes or paths, is the most critical variable, while the slope was considered the least important. No heterogeneity was found regarding the importance of bicycle infrastructure indicators based on gender. However, heterogeneity was observed based on age, daily bicycle use, cycling experience, weekly bicycle use, and bicycle trip purpose. The findings of this research help urban and transport planners develop improvement strategies for the city’s existing bicycling facilities and prioritize future developments by considering various cyclist groups’ preferences.

The psychological distance effect in impact between online review-based tourism destination image and recommendation intention: a data mining approach

ABSTRACT A total of 8,725 reviews of three East Asian travel cities were collected from TripAdvisor, and, after extracting and quantifying the words corresponding to the tourism destination image (TDI), the causal relationship with the recommendation intention (RI) was verified. An integrated data analysis on the data from the three cities revealed that all images except cleanliness among TDI had a significant influence on RI. The Seoul review data analysis revealed the environment, culture/history, and travel plan factors at the low-level influenced RI, while the Seoul Palace review data analysis revealed that society, culture/history, transportation, and travel plan factors at the low-level influenced RI.

Polycentricity and TOD-ness synergy: A novel composite index for integrated development of employment centers and residential cores in transit-oriented neighborhoods

Proximity of transit-oriented development (TOD) goals to polycentric urban development in the field of controlling negative effects of urban sprawl reinforces polycentricity and TOD-ness spatial synergy. Furthermore, identification of transit-oriented neighborhoods that have high potential to become secondary employment centers (ECs) is one of the most important challenges facing urban transport planners and land use policy makers. This research explained integrated development of ECs and residential cores (RCs) in transit-oriented neighborhoods using a novel composite index. The index is designed based on TOD-ness and polycentricity measures using spatial statistical methods and helps urban planners and policy makers to better identify polycentric TODs as transit-oriented residential neighborhoods with high potential to become secondary employment centers. Research findings confirmed higher accessibility of polycentric TODs to public services and public and private transportation that have better integration in the development of urban facilities and transportation systems. Furthermore, the findings confirmed polycentricity and TOD-ness spatial synergies in polycentric TODs.