Transit Planners Research Articles

Resilience of socio-technical transportation systems: A demand-driven community detection in human mobility structures

Existing scholarship on transportation resilience analysis has primarily focused on engineering resilience, often overlooking the intricate socio-technical dimensions. This oversight underscores the necessity for a more comprehensive understanding of the dynamic interplay between social, including travel behaviors, and technical infrastructure components within transportation systems. This article delves into the impact of “social shocks” on transportation systems, which are defined as disturbances affecting the social subsystem without yet affecting the technical subsystem. Drawing inspiration from C.S. Holling’s ecological resilience, which signifies a system’s ability to cope with change by adapting its structure and functionality, we propose a multi-level resilience assessment framework. It encompasses four mobility-related indicators: entropy (measuring network-level complexity), stationarity (assessing community compositional changes at the cluster level), and two node-level metrics — within-module degree and weighted participation coefficient — capturing location connectivity. These indicators proxy for evaluating the mobility structure and node functionality within the social subsystem. In a case study, we analyze historical smart card data to examine the mobility pattern’s structural changes within Hong Kong, a rail-oriented metropolis, during a prolonged and city-wide protest. The framework and associated indicators provide an alternative perspective for transit planners and operators, allowing them to assess both the overall system and individual stations, moving beyond traditional assessments of service supply and patronage changes.

A sequential transit network design algorithm with optimal learning under correlated beliefs

Mobility service route design requires demand information to operate in a service region. Transit planners and operators can access various data sources including household travel survey data and mobile device location logs. However, when implementing a mobility system with emerging technologies, estimating demand becomes harder because of limited data resulting in uncertainty. This study proposes an artificial intelligence-driven algorithm that combines sequential transit network design with optimal learning to address the operation under limited data. An operator gradually expands its route system to avoid risks from inconsistency between designed routes and actual travel demand. At the same time, observed information is archived to update the knowledge that the operator currently uses. Three learning policies are compared within the algorithm: multi-armed bandit, knowledge gradient, and knowledge gradient with correlated beliefs. For validation, a new route system is designed on an artificial network based on public use microdata areas in New York City. Prior knowledge is reproduced from the regional household travel survey data. The results suggest that exploration considering correlations can achieve better performance compared to greedy choices and other independent belief-based techniques in general. In future work, the problem may incorporate more complexities such as demand elasticity to travel time, no limitations to the number of transfers, and costs for expansion.

Development of a dynamic traffic microsimulator for on-demand transit operations within an integrated modelling system

ABSTRACT This study develops an intelligent dynamic agent-based microsimulation (iDAMS) module for on-demand transit (ODT) operations within an integrated transport, land-use and energy (iTLE) model. A real-time optimization component within the iDAMS is formulated by the utilization of travelling salesman problem and simulated annealing metaheuristics that perform dynamic passenger-vehicle assignment. It simulates ODT operations for meeting the 24-hour auto-trip demand of Halifax, Canada, to compare the performance of the proposed system with personal cars (PCs). The optimization objectives are to determine the optimal fleet size and seat capacity that satisfies maximum trip requests while minimizing waiting time, travel time and vehicle kilometres travelled (VKT). Simulation results indicate that the ODT system can deliver service similar to PCs in Halifax while decreasing cost and emissions (13% reduction in VKT). The tools developed in this research will provide transit planners ability to conduct ODT scenario simulation and test system performance in real time.

If You Cut It Will They Ride? Longitudinal Examination of the Elasticity of Public Transport Ridership in the Post-Pandemic Era

In the past two years, public transport ridership has declined because of COVID-19 pandemic health measures and new working from home policies. This decline in ridership has caused major financial stress on public transport agencies around the world. Several agencies have responded to this financial stress by reducing services. The extent to which these service cuts will affect transit ridership is unknown because of the changing operational environment in the post-pandemic world. Our study uses a longitudinal panel data from Montréal, Quebec, Canada, to explore the relationship between route-level ridership and operational factors over time. We find that public transport ridership demand at the route level is highly elastic when compared to trip frequency and has become more elastic after the COVID-19 pandemic. Our findings imply that agencies cutting service in the post-pandemic era run a much more significant risk of creating a “doom spiral,” where service reductions spur greater declines in ridership, forcing further reductions. Demand was found to be most elastic on more frequent routes, so agencies should prioritize maintaining services on their core routes in the post-pandemic era. This study can be of use to public transit planners and policymakers considering making service changes to attract more riders or trying to respond to post-COVID-19 financial stress.

Integration of urban aerial cable cars into public transit: Operational capacity limits due to passenger queuing at stations

As the airspace is increasingly gaining importance as a new frontier to improve urban mobility, aerial cable cars are being discussed and already appropriately implemented worldwide to supplement conventional modes of transport in urban areas. Transit planners and designers should carefully consider the interoperability and integration of cable car services with conventional modes of transport. In particular, excessive delays and overcrowding conditions due to deficits in interoperability should be avoided. This challenge of interoperability arises as conventional modes of transport operate predominantly on a timetable, and most cable car technologies operate in such close headways that they can be considered as almost continuous conveyors. The advantage of having almost always a transport vessel of a cable car ready for boarding ceases when large volumes of passengers arrive in batches, for example from higher-capacity modes of transport or at large events, resulting in long queues. Traditional manuals do not yet reflect these aspects of interoperability adequately. Consequently, this work filled this research gap about the interoperability of cable cars related to handling high volumes of incoming passenger arrivals that transfer in larger batches from feeder modes and often result in queues at cable car stations. The following objectives were targeted: (1) determine passenger capacity limits of conventional modes of transport acting as feeders to cable cars and (2) specify space requirements to be provided due to the potential queues that arise. To answer these, methods of Queuing Theory were used and results were placed in Levels of Services of traditional manuals. Key performance indicators included queue length, waiting time, and corresponding queue space. The results revealed that cable cars can be a useful complement to public transit but are of limited feasibility due to cumulative queues at arrival rates with larger crowds. High-capacity feeder modes (e.g., commuter rails) are limited to 20-minute headways depending on cable car technologies. Further, queuing areas of up to 1000 square meters (around 10,800 ft2) should be considered. Several operational limitations are presented as guidance for practitioners and policymakers.

Why are people leaving public transport? A panel study of changes in transit-use patterns between 2019, 2021, and 2022 in Montréal, Canada

The outbreak of COVID-19 caused unprecedented declines in public-transport use. As travel frequencies rebound, ridership is recovering, although it remains considerably below pre-pandemic levels. This study compares pre- to post-pandemic public-transit use among workers and non-workers, and the changing impact of local and regional accessibility. Additionally, we assess the impact of increased telecommuting on workers’ transit use before, during, and after the pandemic. We estimate two weighted multilevel linear regressions using a three-wave panel survey over the years 2019–2022 in Montréal, Canada. Results indicate that the factors that determine workers’ and non-workers’ transit patterns have tended to diverge after the pandemic. For workers, the relevance of accessibility in promoting utilitarian transit use considerably decreased, being responsible for close to 10% of the post-pandemic transit-use reduction. The increase of telecommuting frequency due to the pandemic contributed more than 10% of the post-pandemic transit-use reduction, but the effect of transit commuting time has remained relevant. For non-workers, the effect of regional accessibility by transit has increased after the pandemic, which has partly mitigated non-workers’ transit-use decline. Moreover, we find there is a joint effect of local and regional accessibility that has maintained after 2019 for non-workers. Results from this work have relevant implications for transit planners and policymakers. To help transit-use recovery, results suggest that providing good transit connection to the workplace promotes workers’ transit use, while promoting transit accessibility in lower-local-accessibility areas is key for non-worker transit ridership.

A Multi-Objective Mathematical Programming Model for Transit Network Design and Frequency Setting Problem

In this study, we propose a novel multi-objective nonlinear mixed-integer mathematical programming model for the transit network design and frequency setting problem that aims at designing the routes and determining the frequencies of the routes to satisfy passenger demand in a transit network. The proposed model incorporates the features of real-life transit network systems and reflects the views of both passengers and the transit agency by considering the in-vehicle travel time, transfers, waiting times at the boarding and transfer stops, overcrowding and under-utilization of vehicles, and vehicle fleet size. Unlike previous studies that simplify several aspects of the transit network design and frequency setting problem, the proposed model is the first to determine routes and their frequencies simultaneously from scratch, i.e., without using line and frequency pools while considering the aforementioned issues, such as transfers and waiting. We solve the proposed model using Gurobi. We provide the results of what-if analyses conducted using a real-world public bus transport network in the city of Kayseri in Türkiye. We also present the results of computational tests implemented to validate and verify the model using Mandl benchmark instances from the literature. The results indicate that the model produces better solutions than the state-of-the-art algorithms in the literature and that the model can be used by public transit planners as a decision aid.

Optimization of headway and bus stop spacing for low demand bus routes

ABSTRACT We propose a methodology for optimization of service headway and stop spacing along a low-demand bus route that minimizes operator and user costs. This study develops analytical cost models that are representative of low-demand routes by using negative binomial distribution for passenger demand for boarding and alighting pattern to estimate the probability of stopping and both random and planned arrival of passengers are considered to estimate the waiting time. Pareto optimal solutions obtained using multi-objective evolutionary algorithm, NSGA-II indicate that optimal values of headway and stop spacing are underestimated if optimized based on assumptions typical of high-demand routes which is passenger demand for boarding and alighting at bus stops randomly following a Poisson process. With the aid of the study methodology, transit planners will be able to improve the service utilization and passenger accessibility along an under-performing low demand routes by recommending minimal modifications to the existing route and bus schedule.

An open-source program for spatial decomposition of bus transit networks

Comparing transit operations and performance over time is often challenging due to changes in route identifiers and service patterns. It is particularly difficult if the analysis period spans a major network design change where bus routes are realigned and renamed. This paper presents a new open-source software package that simplifies longitudinal transit performance analysis by decomposing a bus network into block-length segments that remain stable over time. The package then matches transit networks across time periods using segment geometry rather than potentially unstable route or stop identifiers. This package provides an efficient method for transit planners, advocacy groups, policy makers and urban scientists to analyze changes in transit performance over time without any prior knowledge of network design changes.

Impacts of real-time transit information on transit accessibility – A case study

Transit service accessibility improvement is an important topic among transit planners and policymakers. Real-time transit information (RTI) sharing with the users has several benefits, such as lower wait time at the transit stops and higher transit accessibility. Previous studies assessed the benefits of RTI sharing mostly by analyzing the survey data and simulation-based studies. This study used field trip GPS data to quantify the transit stop wait time reduction in the presence of RTI sharing. A smartphone app was developed in this study to disseminate RTI to the users (i.e., location, speed, weather, and road condition information). The trip data showed that the average reduction of transit stop wait time after the RTI use was about three minutes on a selected transit route in Morgantown, WV. The calculated coverage area improvement indicates that the network-level stop coverage can be increased by 37 % with the use of RTI. The reduced wait time at transit stops reduced the average trip time, and riders can walk from further distances to reach the transit stops. The findings of this study justify more widescale use of the RTI sharing in an urban and rural environment.

Revising bus routes to improve access for the transport disadvantaged: A reinforcement learning approach

Traditional bus route design tools mainly aim to minimise a cost-based objective such as passenger travel times and/or operator costs. These approaches favour high-demand areas in distributing transit services and benefit densely populated areas. Public transport must provide viable transport services to socially disadvantaged people to allow them to undertake essential social and economic activities. This study uses a transit accessibility measure in the route design objective and demonstrates its impacts and implications through a case study in Penrith, an area in Sydney, Australia. The results reveal a limitation of using accessibility as the sole design objective. An equity measure called the transit service need index is incorporated in the objective to revise and redistribute bus services to improve accessibility in areas with more transport disadvantaged people. Reinforcement learning is used for bus route optimisation. The route design tool could be useful for transit planners and operators in route planning and evaluation. The study demonstrates some different approaches of route design objective formulations to meet increasingly diverse needs and expectations for public transport.

A Multiobjective Optimization Model for Flexroute Transit Service Design

Flexroute transit (also referred to as route-deviation) is an innovative public transportation approach in which service is provided at fixed stops on a predetermined schedule, while also providing “on-demand” service to customers between the fixed stops. A significant challenge to the implementation of flexroute transit has been the complexity inherent in the design process. This research explored two of the key design parameters available to a transit planner: service zone size (the area between fixed stops where deviations are permitted), and slack time distribution (the method used to distribute among zones the total “slack” time built into the schedule to allow for deviations). It was found that the objectives of maximizing feasible deviations and minimizing unused slack time are conflicting and highly sensitive to the design parameters. To assist in the design process, the article formulates a multiobjective binary optimization model that can be used to develop an efficient frontier—guiding transit planners as they seek to explore design trade-offs.

The Effects of Articulated Buses on Dwell and Running Times

Articulated buses are being operated more frequently on popular bus routes, as they can handle higher passenger loads and increase rider comfort. Dwell and running times associated with articulated buses are expected to be different from regular low-floor buses. We use archived bus operation and passenger information from three heavily-used bus routes operated by the Société de Transport de Montréal, Canada, to measure these differences. Operation of articulated buses yielded to savings in dwell time, especially with high levels of passenger activity and the use of the third door in alighting. These savings were not reflected in running time, due to increases in the time associated with acceleration, deceleration, and merging with traffic. This study gives transit planners and operators important information on the differences in operating environments between regular and articulated buses.

A geospatial workflow for the assessment of public transit system performance using near real‐time data

AbstractThis article presents the development of a Geographical Information Systems (GIS) workflow that harvests high‐volume and high‐frequency near real‐time data from a public General Transit Feed Specification (GTFS) and calculates metrics for the assessment of on‐time and route speed performance for a public transit system. The approach is applied to near real‐time and static GTFS data collected over a 9‐month period for the City of Calgary, Alberta, Canada. The workflow uses two Azure Virtual Machines (VMs), one to harvest the data and the other to process observations in parallel using Python and the ArcGIS API libraries. A Web GIS application is described that queries data from MongoDB to visualize the performance results in spatiotemporal form. The purpose of the workflow and Web GIS application is to provide actionable information to transit planners to improve public transportation systems. The data management and analysis workflow is transferable to similar GTFS data from other cities.

Measuring the accessibility of metro stations in Tianjin: an origin-destination approach

ABSTRACT The achievement of good spatial accessibility is one of the supreme goals for metro transit planners. While many studies have measured either the accessibility of metro networks or walk accessibility of metro stations, the literature provides limited knowledge for metro station accessibility evaluations integrating both in the context of the origin-destination pair. The present study fills this gap through the use of a set of integrated measures that consider both by-metro accessibility and to-metro accessibility in combination and explores accessibility-based typology among stations. The space syntax is used to conduct the measure models for each dimension (by-metro and to-metro), and typology among stations is divided by an SOM (self-organizing map). The new approach is applied to the case of Tianjin, China. We find that the by-metro accessibility declines from the urban centre to the outskirts of the city and depends mainly on the location in the metro network and the topological depth from the transfer stations. However, the to-metro accessibility varies widely and depends on the street network structure in the station catchment area. Six typologies are identified among stations based on the different characteristics of by-metro accessibility and to-metro accessibility.

A framework for BEB energy prediction using low-resolution open-source data-driven model

• We proposed a low-resolution open-source data-driven BEB energy prediction model. • The prediction model is applied to one-minute segments and full trips. • A sensitivity analysis is conducted to assess the model performance. • On full trips, the model is very accurate (more than 90% accuracy). • On one-minute segments, the model is acceptable (more than 80% accuracy). The accurate calculation of the energy consumption (E C ) rates of Battery-Electric Buses (BEBs) might be ambiguous due to uncertainties in amassing real-world operation data such as speed profiles, route topology, passenger loading, and weather conditions. Therefore, attaining an extensive validated E C prediction model is essential to surmount the challenges in collecting real-world data. In this respect, this study develops and assesses an open-source low-resolution data-driven framework to estimate BEB's E C in transit operation, using vehicular, operational, topological, and external parameters. Moreover, a three-step validation process is used to assess the proposed framework's performance. The results show that the prediction model provides a reasonable error margin (20%). The validation analyses show a powerful goodness-of-fit where the prediction model can explain more than 90% of the E C variation. The framework can superbly provide transit planners and agencies with an optimal transit operating profile that reinforces the BEB’s energy savings.

Canadian transit agencies response to COVID-19: Understanding strategies, information accessibility and the use of social media

Over the past few months, transit agencies across Canada have been rushed to implement a range of strategies in response to the COVID-19 pandemic, with no standardized guidelines to direct their efforts. This study explores the initial response of transit agencies serving the 25 most populous Canadian cities by understanding the distinct types of response measures implemented between March 1st and June 1st, 2020. It also explores to what extent information related to these measures was accessible and usable, and how transit agencies used social media to communicate their efforts to the public. To achieve these goals, a detailed review of Canadian transit agencies websites and social media accounts was performed. The findings suggest that larger transit agencies across Canada implemented the most measures to respond to COVID-19, but not necessarily provided the most accessible information regarding the measures. Overall, while all transit agencies reduced the offered service’s frequency and capacity and enhanced vehicle cleaning, the implementation of other physical and communication measures varied considerably between agencies. Information related to the number of COVID-19 cases within the workforce was least accessible across agencies. Transit agencies’ Twitter platforms were used more by larger agencies. While most of transit agencies tend to employ tweets that include some type of graphics, very few agencies employed videos and animations to communicate important information to the public. This paper provides transit planners and policymakers with comprehensive information regarding the initial response of Canadian transit agencies to maintain operations in such critical times.

The bus-type taxi: a better demonstration of ride-sharing than Uber Taiwan

ABSTRACT This study addresses a capacitated vehicle routing problem with time windows and simultaneous pickup and delivery (VRPTWPD) for scheduling bus-type taxis formerly served by regular buses. The effects of varying the maximum number of passenger seats on service quality and costs are investigated. Taiwan's government has subsidized public transport in order to ensure consistency between service shifts and demands, improve the efficiency of public transport operations, and maintain an ongoing supply of services while reducing the number of scooters and/or private cars on roads to minimize traffic congestion. Ultimately, the intent is to reduce government subsidies further and maintain service quality at reasonable levels. Transit planners have also introduced the Demand Responsive Transit System (DRTS) to connect routes. Particularity in rural areas, local transit planners have replaced buses with taxis to form a bus-type taxi service that increases efficiency. As a consequence, the transit service in low-density or rural areas has not been abandoned. The study acknowledges that optimal routes for successful bus-type taxis services vary by area. Routing and scheduling results will assist route planners' efforts to provide sufficient transit services in rural areas. The study results also suggest that further reductions in government subsidies are possible.

Station facilities and noise assessment: A multilevel analysis on light rail train stations

Noise is one of the most frequent consequences of traffic. Public transportation systems, such as the Dallas Area Rapid Transit (DART) authority provides various modes of transportation. Even though the availability of commuting service for the public is a boon to communities, mass transit systems are potential sources of excessive sound levels in daily urban life. This article examines the nexus between the transit station facilities of light rail train (LRT) stations and noise implications at both station and neighborhood scales by studying selected LRT stations. A multilevel linear analysis was conducted to understand the degree of train station amenities and neighborhood characteristics that affect sound levels. Using a type II sound pressure level (SPL)meter, sound measurements were obtained during the weekdays and weekends over several weeks. Upon examining the station amenities, and built environment and sociodemographic characteristics of the neighborhood, findings of this comprehensive research reveal significant implications for sound levels. Stations with ticket vending machines and informative message boards include a higher degree of significance on SPLs, while shelters, crew rooms, bike lockers, restrooms, and windshields are significantly and negatively associated with the noise levels. Additionally, neighborhoods with dense roads, higher speed limits, more neighborhood facilities, and a higher number of transit routes have an increased likelihood of noise levels. Recommendations include creating transformative policies for implementation, and approaches addressing noise for transit authorities, transportation engineers, and planners are presented. Planning and engineering aspects of comfort, aesthetics, safety, and public health, as train stations are daily use spaces for commuters and surrounding communities, should also be considered.

A cross-city exploratory analysis of the robustness of bus transit networks using open-source data

A robust bus transit network is of fundamental importance for sustainable development by alleviating urban problems. This paper aims to explore the robustness of 57 bus transit networks from the aspect of transferability. Bus transit networks are constructed using open-source data from the same data source for ensuring a consistent comparison, and the network robustness is analyzed using giant component (GC) to represent the maximum scale of transferability and network efficiency (NE) that characterizes the overall efficiency of transferability. (1) The results reveal a universal heavy-tailed distribution of network betweenness irrespective of cities and indicate target attack is more destructive to network robustness than random attack. (2) Different cities have different degrees of robustness, where most large cities tend to be more vulnerable than small cities and NE is more likely to be affected by target attack than GC. (3) The impact of target attack may become weaker than random attack after removing a certain percentage of nodes, which varies in different cities. (4) Thereafter, we present clusters of cities according to similarities of their network robustness. Thus, our comparative results can benefit transit planners and policymakers by enhancing the robustness of bus transit networks.