Transportation Planning Research Articles

Transforming Urban Environments: Understanding the Social Implications of Metrobus (MBS) Service Development in Lahore, Pakistan

This research investigates how the Lahore Metrobus system (MBS) influences perceptions of urban space quality through the lens of urban design and transport infrastructure. Lahore is undergoing significant urban changes with the introduction of a newly constructed mass transit system, which thus necessitates a joint development strategy to preserve its cultural heritage. Recent evolutions in transport planning has heightened the interest in analyzing the way mobility factors affect the perceptions of a place’s quality. The gap in previous work lies in the limited focus on qualitative, human-centered perspectives regarding the impacts of public projects like the MBS on urban space quality. This study uniquely fills this void by examining the influence of the MBS on people’s lives and the quality of urban spaces. Comparing pre- and post-Metrobus scenarios, along with questionnaire surveys of riders and shopkeepers, exposes the neglect of the social image during design and construction. The system was implemented with a focus solely on hard, core infrastructure, thereby neglecting soft components such as area development, social and cultural value, and human-centered design. The government should unite transport agencies, stakeholders, and the public to craft a joint policy for enhancing revenue, ridership, and fostering transit-oriented development (TOD). Research findings will help in achieving social and cultural sustainability for upcoming transport lines in Pakistan, as well as directions for other developing nations looking to implement mass transit networks.

Security Analysis of Pedestrian Comfort on the Use of Pedestrian Paths in the Bung Hatta Palace Area

Many pedestrian paths are still not well organized and optimally utilized. On the other hand, the comfort of pedestrian paths should be a priority in urban transportation planning. This study aims to determine the level of pedestrian service on Jl. Istana and Jl. H. Agus Salim Bukittinggi city and to determine the perceptions of people who use pedestrian crossing facilities on Jl. Istana and Jl. H. Agus Salim in the Bung Hatta Palace area Bukittinggi based on comfort and level of pedestrian service. This research uses descriptive research methods carried out to explain the situation that occurs on the pedestrian path Jl. Istana and Jl. H. Agus Salim in the Bung Hatta Palace area Bukittinggi. For the results of pedestrian movement characteristics obtained data Flow (Flow) in segment I amounted to 0.12, 0.70 and 0.75 pedestrians / min, in segment II amounted to 0.29, 1.13 and 1.67 pedestrians / min. Speed data in segment I was found to be 52.32, 55.97 and 54.97 m/min, in segment II it was 55.03, 51.74 and 53.96 m/min. Density in segment I was 0.18, 0.19 and 0.18 m2/ped, in segment II it was 0.62, 0.57 and 0.60 m2/ped. Space in segment I was 5.65, 5.38 and 5.50 m2/ped, segment II was 1.62, 1.74 and 1.67 m2/ped. Pedestrian ratio in segment I amounted to 0.0016, 0.0094 and 0.0100, segment II amounted to 0.00380, 0.01509 and 0.02227. The results showed that the level of pedestrian service on Jl. Istana and Jl. H. Agus Salim in the Bung Hatta Palace area Bukittinggi is A, because pedestrians can move freely according to their wishes without any interference from other pedestrians and for the results of the questionnaire that has been distributed as many as 100 respondents obtained a comfort value of 72% and this figure is included in the comfortable category.

Toward a Better Understanding of the Construction Impacts of a Light Rail System in Montréal, Canada

Large-scale transport infrastructure projects generate long-lasting changes in the built environment and alter the lives of nearby residents. It is crucial to understand public perceptions of public-transit projects and associated construction impacts, as they influence the social acceptance and eventual success of such projects. To characterize the construction-phase experiences of a new light rail in Montréal, Canada—the Réseau express métropolitain (REM)—we analyzed data from 1,236 respondents from the Greater Montréal region who self-reported ongoing construction activities near their homes. This study employs an exploratory factor and k-means cluster analysis to group residents by their different experiences and perceptions of the REM and its associated construction impacts. The analysis returned five clusters with distinct construction experiences: construction-concerned travelers, REM-critical respondents, neutral travelers, REM enthusiasts, and rerouted travelers. Subsequently, the acceptability of the impacts during the construction phase on each cluster is assessed by comparing perceptions of the impact of neighborhood change on their quality of life and their intention to use the REM. Finally, we derive targeted policy recommendations to help promote increased social acceptability of light-rail transit (LRT) projects, including mitigating disruptions in construction zones, public information campaigns, and inclusive decision-making processes. Findings from this study can benefit policymakers and transport planners as they aim to reduce the disruptions associated with the construction of LRT systems and promote increased social acceptability.

Systematic application of traffic‐signal‐control system architecture design and selection using model‐based systems engineering and Pareto frontier analysis

AbstractThe global population rise has increased vehicles on roads, complicating traffic management. Inefficient traffic control systems cause significant economic losses owing to commuter time wastage, high energy consumption, and greenhouse gas emissions. Traffic signal control systems (TSCSs) are vital in traffic management, impacting traffic flow significantly; therefore, studies are exploring new optimization approaches that adapt to changing traffic conditions. However, they concentrate on either new technology infusion or on control algorithm optimization, and do not holistically address the architectural configuration of the system. In this study, we presented a unique case study by applying an existing systematic framework to the TSCS system architecture design and selection process. This application demonstrates that TSCS enhancement is a multifaceted process that requires a comprehensive assessment of not only technical aspects, such as the control algorithm, but also factors including system architecture, security, and data integrity. Because of the increasing reliance of TSCSs on data exchange between their various subsystems, this case study also adopted a cybersecurity perspective of the system and introduced cyber resiliency as a crucial metric for evaluating TSCS architecture performance. Furthermore, through the applied framework, an optimal TSCS architectural configuration with executable options was identified by generating multiple TSCS architectural configurations using decision option patterns and identifying those on the Pareto frontier to understand the architectural decision‐making process. Traffic engineers and transportation planners can use this case study application as a guide to optimize TSCSs employed in existing transportation networks and design more efficient transportation networks for future urban development.

A multi-channel spatial-temporal transformer model for traffic flow forecasting

Traffic flow forecasting is a crucial task in transportation management and planning. The main challenges for traffic flow forecasting are that (1) as the length of prediction time increases, the accuracy of prediction will decrease; (2) the predicted results greatly rely on the extraction of temporal and spatial dependencies from the road networks. To overcome the challenges mentioned above, we propose a multi-channel spatial-temporal transformer model for traffic flow forecasting, which improves the accuracy of the prediction by fusing results from different channels of traffic data. Our approach leverages graph convolutional network to extract spatial features from each channel while using a transformer-based architecture to capture temporal dependencies across channels. We introduce an adaptive adjacency matrix to overcome limitations in feature extraction from fixed topological structures. Experimental results on six real-world datasets demonstrate that introducing a multi-channel mechanism into the temporal model enhances performance and our proposed model outperforms state-of-the-art models in terms of accuracy.

Enhancing parameter calibration for micro-simulation models: Investigating improvement methods

Calibrating microscopic traffic simulation models is a prerequisite for simulation applications. This study proposes three novel methods to improve the accuracy and interpretability of the calibration model. The proposed approach involves selecting the calibration parameter, refining the model parameter system, and optimizing the calibration results. The first method expands the single-point mean into a multi-point distribution. The cumulative distribution curve of delay was selected as the calibration parameter. The second method divides the parameter system into global and local parameters. Global parameters were calibrated using NGSIM measured data, and local parameters were calibrated through intelligent algorithms. The third method proposes a methodology of parameter clustering recursion based on the genetic algorithm results, with information entropy selected as the analysis index. To evaluate the effectiveness of the proposed optimization methods, this study used NGSIM trajectory data as a case study. Eight simulation schemes based on the three optimization methods were designed, and simulation experiments were conducted using the VISSIM platform. The results show that the accuracy of the multi-point distribution calibration and parameter value optimization method is significantly higher than the default method. Additionally, the optimization method with calibration of both global and local parameters was more consistent with actual driving characteristics. This study provides a theoretical foundation for improving the practical application of traffic simulation technology, which has significant implications for transportation planning and management.

SIMULATING THE EFFECTS OF EURO 4 DIESEL-POWERED ENGINE JEEPNEYS ON AIR QUALITY AND UNDERSTANDING ESTIMATED HEALTH RISK TOWARDS SUSTAINABLE TRANSPORTATION

Abstract. The Euro-4 diesel-powered engines are the current industry standard in the Philippines as indicated in the Public Utility Vehicle Modernization Program (PUVMP) Omnibus Guidelines Department Order 2017-011. Based on the National Emissions Inventory report of the Environmental Management Bureau in 2018, 74% of air pollutant concentrations were produced by mobile sources including jeepneys. The PUVMP is focused on enhancing environmental sustainability, air quality, and in providing a reliable and effective public transportation system. The potential advantages to air quality and health from the full implementation of the program especially the Euro 4 diesel-powered engine jeepneys in Trece Martires City are assessed in this paper. The study aims to estimate the impact of the possible enforcement of Euro 4 diesel-powered engine jeepneys on the reduction of air pollution in the research locale. Four pollutants were assessed particularly Carbon Monoxide (CO), Nitrogen Oxide (NOx), Sulfur Oxide (SOx) and Particulate Matter 2.5 (PM2.5) using AERMOD Air Dispersion Modeling Software in five (5) different scenarios: (1) baseline engine jeepneys, (2) 25% implementation of Euro 4 diesel-powered engine jeepneys, (3) 50% implementation of Euro 4 diesel-powered engine jeepney, (4) 75% implementation of Euro 4 diesel-powered engine jeepneys and (5) full implementation of Euro 4 diesel-powered engine jeepneys. AirQ+ was utilized to evaluate the health risk of Euro 4 diesel-powered engine jeepneys to the drivers and commuters in the vicinity. Result shows that the Scenario 5 or the full implementation of Euro 4 diesel-powered engines relatively reduces CO by 84.30% (8.355 μg/cu.m to 1.31 μg/cu.m), NOx by 21.76% (10.6 μg/cu.m to 8.27 μg/cu.m), and PM2.5 by 83.36% (2.69 μg/cu.m to 0.447 μg/cu.m), by its maximum estimated concentrations. However, an increase by 14.14% of SOx emissions (0.091 μg/cu.m to 0.104 μg/cu.m) were seen on the simulation from Scenario 1 to Scenario 5. In terms of health risk assessment, higher levels of PM2.5 exposure exhibit an increase in health hazard with respect to mortality rate. Simulation outputs indicate that transitioning of jeepneys from current technology to Euro 4 models reduces pollution levels and lowers health risk, hence, can be used as basis in enhancing transport plans and health monitoring strategies in the city.

Early Perspectives: Exploring the Potential Impacts of Autonomous Vehicles Through the Lens of Urban Mobility and Urban Form

The emergence of autonomous vehicles (AVs) is expected to significantly reshape urban mobility and travel behavior patterns. AVs have the potential to offer higher levels of convenience, safety, and accessibility while enabling users to spend their trip time on more efficient and productive tasks such as working or even relaxing. This transformation in the short- to mid-term could result in changes in the public's sensitivity and perception toward enduring longer travel times and consequently, in mid- to long-term, it could influence the willingness to reconsider their residential locations. Therefore, the objective of this study is to enhance the understanding of the potential effects of AVs on travel behavior and land use through the examination of stated preference queries. To achieve this objective, various multinomial logit models toward AVs adoption and residential relocation were estimated by employing a dataset from Istanbul, Türkiye, as an example of megacity in a developing country. While the study findings revealed a set of potential adoption barriers for AVs, they also indicated a notable propensity for adopting these vehicles. Furthermore, concerning individuals' willingness to reconsider their residential locations due to the emergence of AVs, a considerable positive attitude was evident, albeit guarded. This attitude suggests that individuals' decisions are still evolving and can be guided toward the desired future through well-timed and well-suited policies. The outcomes of the study can serve as valuable input for policymakers as well as transportation and urban planners, offering insights into the potential impacts of AVs on urban mobility and form.

Developing a statewide active transportation planning dashboard with mobility data

ABSTRACT Existing walking/biking data are often insufficient to support active transportation planning activities. This study used an emerging large-scale human mobility dataset to identify places where there are a higher number of short-distance trips to public places (expressed by a mobility index). Demographic variables were integrated into the mobility index to prioritize access for more people and address equity concerns. In addition, a safety index (reflecting crash injuries/fatalities involving pedestrians/bicyclists) and a connectivity index (reflecting the density of existing active transportation infrastructure) were also considered together with the mobility index in generating an investment potential score. All the indices/scores were mapped at hexagon and segment level on an online dashboard. The developed dashboard is expected to support statewide active transportation planning and project selection/prioritization decision-makings. The proposed methodology is replicable for any places that have few active transportation facilities and where pedestrian/bicyclist count data are not sufficient to model demand directly.

Collaborative optimization of routing and storage strategy of multi-period multimodal transport in an uncertain environment

This study uses a storage service strategy to investigate a multiperiod multimodal transport routing and transit planning problem (MPMT-RTP). First, a mathematical integer programming model is used to describe the problem in a deterministic environment. Second, two robust optimization models are established to address the uncertainty of the freight rate fluctuations in each period and the storage space in the total set, thereby improving the solving efficiency of the model through a robust equation transformation. Subsequently, hybrid heuristics are proposed to solve the above problems, and a series of small-scale experiments are conducted to verify the accuracy of the model and effectiveness of the algorithm. Finally, this study conducts case experiments based on survey data. The results show that the total operating cost is reduced by 13.31 % via strategic multiperiod multimodal transport planning. A price fluctuation study shows that under different price fluctuations, the transportation and storage periods of some orders change accordingly, and under three specific price fluctuations, the operating costs are 1085100, 967330, and 948770, respectively. Therefore, the results of this study can provide theoretical guidance for enterprises to optimize their resource allocation paradigms and better adapt to freight rate fluctuations to minimize their operating costs.

A Review of GIS Technology Applications in Transportation Planning and Management

With the rapid development of the economy, the scale of urban construction is expanding, the corresponding transportation infrastructure is also improving, and the traffic congestion problem is becoming more and more serious. The application of GIS technology to transportation management can effectively alleviate the problem of urban traffic congestion at this stage, thus improving the operation of road traffic. This paper describes the advantages of GIS technology in transportation planning and management from the aspects of accuracy, measurement convenience, visualization, scientific, dynamic, data management, and introduces its application initiatives in road design, traffic prediction, vehicle guidance, traffic system design, traffic analysis. The application of GIS technology in transportation planning is becoming more and more extensive and profound, the technology can provide ideas and technical support for modern transportation planning, but also can combine the construction of road transportation system with information technology, effectively improve the rationality and scientificity of the transportation planning program, and play a positive and significant role in reducing traffic congestion and improving the efficiency of traffic operation.

Evaluating the cost-emissions trade-offs of a modal shift in intermodal and synchromodal transportation planning

Abstract Accepted by: Aris Syntetos Intermodal transportation planning combines road with more sustainable transportation modes to encourage a modal shift. To evaluate the impact of a modal shift on transportation cost and emissions, we propose an intermodal transportation planning model to provide transparency in the cost-emissions trade-off. The model incorporates minimum load requirements, time windows, freight consolidation and stochastic travel times to generate alternative transportation options. It also includes order consolidation to facilitate the utilization of transportation modes that would otherwise be infeasible due to, for instance, minimum load requirements. We also propose a synchromodal planning tool to evaluate re-planning and re-consolidation options in response to disruptions. We numerically illustrate the working of our model using a representative network setting and quantify the trade-offs concerning costs and emissions by evaluating different transportation route options.

Understanding and including 'pink-collar' workers in employment-based travel demand models.

The segmentation of commuters into either blue or white-collar workers remains is still common in urban transport models. Internationally, models have started to use more elaborate segmentations, more reflective of changes in labour markets, such as increased female participation. Finding appropriate labour market segmentations for commute trip modelling remains a challenge. This paper harnesses a data-driven approach using unsupervised clustering-applied to 2017-20 South East Queensland Travel Survey (SEQTS) data. Commuter types are grouped by occupational, industry, and socio-demographic variables (i.e., gender, age, household size, household vehicle ownership and worker skill score). The results show that at a large number of clusters (i.e., k = 8) a highly distinct set of commuter types can be observed. But model run times tend to require a much smaller number of market segments. When only three clusters are formed (k = 3) a market segmentation emerges with one female-dominated type ('pink collar'), one male-dominated type ('blue collar') and one with both genders almost equally involved ('white collar'). There are nuances as to which workers are included in each segment, and differences in travel behaviours across the three types. 'Pink collar' workers are mostly comprised of female clerical and administrative workers, community and personal service workers and sales workers. They have the shortest median commutes for both private motorised and active transport modes. The approach and methods should assist transport planners to derive more accurate and robust market segmentations for use in large urban transport models, and, better predict the value of alternative transport projects and policies for all types of commuters.

Development of a Method for Assessing the Quality of Public Transport Services within Urban Transportation Planning Systems

Development of a Method for Assessing the Quality of Public Transport Services within Urban Transportation Planning Systems

Temporal dynamics of public transportation ridership in Seoul before, during, and after COVID-19 from urban resilience perspective

We delve into the temporal dynamics of public transportation (PT) ridership in Seoul, South Korea, navigating the periods before, during, and after the COVID-19 pandemic through a spatial difference-in-difference model (SDID). Rooted in urban resilience theory, the study employs micro-level public transportation card data spanning January 2019 to December 2023. Major findings indicate a substantial ridership decline during the severe COVID impact phase, followed by a period in the stable and post-COVID phases. Specifically, compared to the pre-COVID phase, PT ridership experienced a 32.1% decrease in Severe, followed by a reduced magnitude of 21.8% in Stable and 13.5% in post-COVID phase. Interestingly, the observed decrease implies a certain level of adaptability, preventing a complete collapse. Also, contrasting with findings in previous literature, our study reveals a less severe impact, with reductions ranging from 27.0 to 34.9%. Moreover, while the ridership in the post-COVID phase exhibits recovery, the ratio (Post/Pre) staying below 1.0 suggests that the system has not fully returned to its pre-pandemic state. This study contributes to the urban resilience discourse, illustrating how PT system adjusts to COVID, offering insights for transportation planning.

Entropic transfer operators

We propose a new concept for the regularization and discretization of transfer and Koopman operators in dynamical systems. Our approach is based on the entropically regularized optimal transport between two probability measures. In particular, we use optimal transport plans in order to construct a finite-dimensional approximation of some transfer or Koopman operator which can be analyzed computationally. We prove that the spectrum of the discretized operator converges to the one of the regularized original operator, give a detailed analysis of the relation between the discretized and the original peripheral spectrum for a rotation map on the n-torus and provide code for three numerical experiments, including one based on the raw trajectory data of a small biomolecule from which its dominant conformations are recovered.

Exploring the Landscape of Autonomous Vehicles Research: A Scientometric Analysis in the Context of Urban Transportation Planning

Exploring the Landscape of Autonomous Vehicles Research: A Scientometric Analysis in the Context of Urban Transportation Planning

Rescaling public transport planning in Sweden: investigating the continued planning at the scale ‘left behind’

ABSTRACT This paper aims to contribute to a qualitative understanding of rescaling and its impact on planning strategies and governance relations across scales. By investigating the effects of rescaling for the old scale ‘left behind’ – through the case of public transport planning in Sweden – this paper illustrates how rescaled tasks continue to engage the scale ‘left behind’ (and is a source of ‘tensions’), long after a rescaling process has taken place. Through the lens of rescaling, three main points of discussion are highlighted in the paper: Firstly, processes of rescaling are intertwined with policy layering, and can as such be a source of both ‘good and evil’ for the continued planning on the scale ‘left behind’. Secondly, this calls for an increased geographical sensitivity in research when investigating the effects of rescaling, as the formal and practical outcomes of rescaling can be spatially unequal for planning bodies with similar formal mandates on the same scale. Thirdly, the development of governance relations and ‘tensions’ between new and old scales, are by no means static in time nor space, and calls for increased dialogue across planning scales to aid in the transition of responsibilities from the old to the new scale.

Determinants of irregular demand for regional rail passenger services – case study of High Tatras in Slovakia

AbstractThe demand for public transport by tourists increases significantly in tourist-attractive destinations. This is in addition to regular passengers commuting to school and work. The level of irregular demand is influenced by several factors related to the characteristics of the day of the week, the period of the year, and the current weather. The main goal of the paper is to verify which factors most influence the irregular demand for transport in a tourist-attractive area to ensure operational planning of public passenger transport. Thanks to this, it is possible to ensure sufficient capacity and, at the same time, the efficiency of the operation of public passenger transport. The paper analyzes the main determinants of the irregular demand for regional public rail passenger transport in the High Tatras region of Slovakia. Multiple linear regressions were used to model the number of irregular passengers. The variables representing the day of the week, the attractiveness of the period, and the holiday were found to be the most significant. The variables describing the weather such as maximum daily temperature, precipitation, clouds, and wind had less influence. The obtained mathematical models for forecasting the irregular demand for public passenger transport can help optimize the timetable’s operational setting and the train sets’ size.

Spatial-temporal identification of commuters using trip chain data from non-motorized mode incentive program and public transportation

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