2021

Konstantinos Gkiotsalitis (k.gkiotsalitis@utwente.nl), University of Twente
Oded Cats, Delft University of Technology

Public transport is one of the most disrupted sectors of the COVID-19 pandemic with reported ridership drops up to 90% in majorly affected countries. As many government authorities strive to partially resume activities, public transport operators are in an urgent need for models that can evaluate the impact of different social distancing policies on operational and passenger-related costs. In this study, we introduce a mixed-integer quadratic programming model for the redesign of public transport services considering the operational, passenger, and revenue loss-related costs by evaluating the effects of different social distancing policies. Our model is applied at the metro network of Washington D.C. and provides optimal redistribution of vehicles across lines for different social distancing scenarios. This model can be used as a decision support tool by other policymakers and public transport operators that are in need of evaluating the costs related to the implementation of different social distancing policies.

Md Ahmed, Dhaka University of Engineering and Technology
Md. Hadiuzzaman, Bangladesh University of Engineering and Technology
Md. Rakibul Islam, University of Central Florida
Nafis Anwari, Ahsanullah University of Science and Technology
Md. Abdul Awal Islam, Bangladesh University of Engineering and Technology

Railway passenger perception has been studied extensively in developed countries, however rarely so in developing countries. This study assesses intercity commuter rail passenger perception of railway station facilities using Ordinal Logistic Regression (OLR) in Joydebpur Railway Station, Bangladesh. Data from 1000 respondents were used to evaluate effect of 24 distinct service quality attributes and 5 demographic parameters on passenger satisfaction. Several attributes considered are unique to developing countries, including, Porter behavior, Illegal establishments, and Floating people.  Results show that 13 service quality attributes have significantly affected passengers’ perception. Attribute ranking based on Pearson chi-square test revealed food and soft drinks to be the most significant attribute controlling passenger opinions. Among the five demographic factors, Age, Occupation, and Travel frequency significantly influenced Overall Passenger Satisfaction (OPS). In addition, Pearson correlation matrix reveals moderate correlation among several pairs of service attributes, including Travel frequency-Age, Main road suitability-Level crossing safety, Porter behavior-Food and soft drinks, Departure performance-Arrival performance, Pickpocketing activity-Female safety, and Waiting room facility-Platform crossing facility. The results suggest that policy makers should focus on the elderly, financially solvent people, and frequent travelers.  Besides, refreshment facilities should be given priority, as this will heavily impact passenger satisfaction according to this study. Subsequent attributes can then be prioritized as per the attributes ranked and according to budget considerations of the authority.

Frederik Bachmann, Technical University of Munich
Andreas Rau, TUMCREATE
Fritz Busch, Technische Universitat Munchen

The attractiveness of a system is directly connected to its reliability. The more reliable a system is the more attractive it is for its (potential) users. In public transport this means that more people shift to public means of transport when it gets more reliable. One way to improve their reliability is to mitigate the negative effects of incidents on the public transport service and on its users. This paper introduces a new passenger centric incident management method to mitigate negative effects of incidents. Incidents such as traffic accidents and congestion, ambulance deployment, technical failures and similar events cause service cancellations and delays which disrupt the planned trips of passengers. By redirecting affected passengers onto alternative paths whilst considering capacities to avoid secondary incidents lead to a significant reduction of delays. An additional reduction can be gained by reallocating vacant capacities onto desired alternative paths to support the redirection of passengers logistically. A numerical example is presented, showing the positive effects of the here presented passenger centric method through redirecting passengers and reallocating capacities. These are the first steps towards an optimal solution to passenger centric incident management in public transport.

Leila Hawa, McGill University
James DeWeese, McGill University
Ehab Diab, University of Saskatchewan
Crumley Miles, TriMet
Ahmed El-Geneidy (ahmed.elgeneidy@mcgill.ca), McGill University

Public transit agencies rely on service operations performance measures to guide service improvements efforts. They also collect customer feedback on performance to measure levels of satisfaction among users. Connecting these two performance measures can help public transport agencies increase ridership satisfaction and loyalty. In this study, we link service performance measures obtained from automatic vehicle location (AVL) and automatic passenger count (APC) with customer complaints. This is done while controlling for the amount of ridership to understand if routes with worse service performance, as identified by operations measures, also have more service complaints. We also investigated if an area’s level of affluence affects the number of service complaints per rider that the route receives while controlling for route service performance and ridership. The AVL/APC and customer-feedback data were provided by Portland, Oregon’s TriMet transport agency for the period between August 2018 and January 2019. Descriptive statistics at the route level and a series of mixed-effect multilevel logistic regression models were used to quantify the relationship between route service performance, service complaints, and a service-area vulnerability index, at the route-day level. The likelihood of receiving a service complaint for a route in a day was found to increase based on service performance and the vulnerability of the neighborhood being served by that route, all else held equal. Findings from this research unmask the relationship between service complaints, bus operations and socioeconomic characteristics of the neighborhood a route is serving, offering insights to transport planners and researchers in the psychology behind bus performance complaints.

James Pritchard (j.a.pritchard@soton.ac.uk), University of Southampton
Simon Blainey, University of Southampton
Ben Waterson, University of Southampton

Rail operators, in common with other public transit operators, are under pressure to meet increasing passenger expectations, especially given a step change in the level of information provision and on-board equipment enjoyed by private car users.  Research has shown that a bespoke passenger-centric approach may be necessary to meet these expectations. Personalized services often require users to give up personal data, which may have privacy implications.  Following an overview of possible uses of data in the rail industry and the potential trade-offs with data privacy concerns, details of survey work undertaken on the UK national rail network in January and February 2020 are presented.  The results are compared with an earlier study of road users and their concerns about Intelligent Transport Systems (ITS), which identified four distinct clusters of privacy views, one of which was unique to a transportation context.  The results in this case could be clustered in the same way, although more work needs to be done to understand any links between passenger type (in terms of journey purpose, regularity and so on) and privacy concerns.  The results do however show some general willingness amongst rail passengers to trade privacy concerns with service improvements.  This paper also provides a good basis for future comparative studies to assess the impact of the Covid-19 pandemic on attitudes to data privacy (given that “track and trace” systems have subsequently become ubiquitous, for example).

Aristomenis Kopsacheilis, Aristotle University of Thessaloniki
Ioannis Politis, Aristotle University of Thessaloniki
Georgios Georgiadis, Aristotle University of Thessaloniki

Existing literature has highlighted bus speed and bus arrival time at bus stops as the primary factors that influence the perceived bus service quality by passengers and in turn influence the increase of ridership. The present research proposes an Artificial Neural Network for the prediction of bus commercial speed, based on multiple variables, such as bus dwell time, passenger demand, bus stop location and bus stop infrastructure. The data used in the model’s training process, refer to 10 bus lines of the bus transport system of Thessaloniki, Greece. In succession, the Shapley Additive Explanations algorithm was applied, in order to identify the contribution of the input variables to the outcome. Results indicate a satisfactory model performance, in predicting bus commercial speed. Bus stop spacing was identified as the key contributor to the model’s output; distance of more than 450 meters between 2 successive stops was found to have a positive effect on bus speed. The existence of single-shelter bus stops was linked to higher bus commercial speeds, while the location of bus stops on the middle of a building block proved to affect bus speed in a positive way. Dwell time was also a crucial parameter, having a decreasing effect on bus speed, as its value increases. Additionally, it was found that the increase in the number of traffic lights and turns along a route section, affects bus speed negatively. Finally, no apparent relation was found between total passenger traffic and bus speed.

Konstantinos Gkiotsalitis, University of Twente

As public transport operators try to resume their services, they have to operate under reduced capacities due to COVID-19. Because demand can exceed capacity at different areas and across different times of the day, drivers have to refuse passenger boardings at specific stops. Towards this goal, many public transport operators have modified their service routes by avoiding to serve stops with high passenger demand at specific times of the day. Given the urgent need to develop decision support tools that can prevent the overcrowding of vehicles, this study introduces a dynamic integer nonlinear program that proposes service patterns to individual vehicles that are ready to be dispatched. In addition to the objective of satisfying the imposed vehicle capacity due to COVID-19, the proposed service pattern model caters for the waiting time of passengers. Our model is tested in a bus line connecting the university of Twente with its surrounding cities demonstrating the improvement in terms of vehicle overcrowding, and analyzing the potential negative effects related to unserved passenger demand and excessive waiting times.

Tao Zhang (taoerbuqi007@163.com), Taiyuan University of Science and Technology
Qinglin Jia, Taiyuan University of Science and Technology
Minjie Jin, Taiyuan University of Science and Technology
Fan Sun, Taiyuan University of Science and Technology
Yang Yang, Taiyuan University of Science and Technology

The outbreak of the Corona Virus Disease 2019 has brought unprecedented challenges to urban public transportation. During the epidemic prevention and control period, as an important part of urban transportation, public transport must not only control the risk of epidemic spread, but also ensure the safety of residents. Particularly low-mobility individuals, including generally older adults, individuals with disabilities, and low-income individuals in China, because they often are marginalized by the urban environment and prefer to ride the public transportation. This study uses the characteristics of bus travel, psychological characteristics, and bus satisfaction to analyze the bus needs of low-mobility individuals before and after the Corona Virus Disease 2019, based on a hybrid survey method. The results of the study show that: (a) low-mobility individuals are more difficult to travel by bus than the general public; (b) the bus demand of the three subgroups is different; (c) before and after the epidemic, there are large differences in bus travel between different groups, showing that after the epidemic, different groups are more sensitive to taking public transportation. Furthermore, the special conditions of Corona Virus Disease 2019 should be fully considered to meet the public transport needs of low-mobility individuals when planners formulate public transport travel policies.

Farah Awad (f.awad18@imperial.ac.uk), Imperial College London
Daniel Graham, Imperial College London
Laila AitBihiOuali, Imperial College London

Based on a 12-year unbalanced panel dataset of 33 metros, this paper studies the relationship between the quality of the service and the cost performance of metros. The analysis employs panel econometric models which address endogeneity bias stemming from unobserved heterogeneity and reverse causality. Findings show that quality of service attributes have different directions in their effects on cost performance suggesting that increasing some quality aspects may result in cost gains, while other aspects may entail losses. Results also indicate that cost savings attributed to economies of scale may be overestimated in the literature. Keywords : cost performance, quality of service, urban rail, performance assessment, efficiency

MD Sultan Ali, Florida International University
John Kodi, Florida International University
Priyanka Alluri, Florida International University
Thobias Sando, University of North Florida

This study evaluated the operational performance of Transit Signal Priority (TSP) in an Adaptive Signal Control Technology (ASCT) environment using PTV Epics/Balance local controllers in a microscopic simulation environment. The analysis was based on a 4-mile study corridor in Florida. Three microscopic simulation VISSIM models, a Base model, a TSP model, and TSP operating in ASCT environment (ATSP) model were developed. The Base model was calibrated to represent field conditions. The ATSP model provided significant savings in travel time and average vehicle delay compared to the Base and the conventional TSP model operating in actuated conditions. Under the ATSP scenario, the study corridor experienced up to 13.4% reduction in travel times for buses and all other vehicles, and up to 11% reduction in average vehicle delay for buses and all other vehicles. The results were statistically significant at a 95% confidence level. To better quantify the mobility benefits of ATSP, Mobility Enhancement Factors (MEFs) were estimated. MEFs are multiplicative factors used to estimate the expected mobility level of ATSP. A MEF < 1 implies that ATSP yields mobility benefits. The MEFs based on travel time for buses and all other vehicles were estimated to be 0.878 and 0.910, respectively. The MEFs based on average vehicle delay for buses and all other vehicles were 0.896 and 0.923, respectively. The ATSP model was also found to improve cross-street delay. The study findings may provide transportation agencies with a deeper understanding of the potential of the Epics/Balance controllers in improving traffic operations.

Nicholas Caros (caros@mit.edu), Massachusetts Institute of Technology (MIT)
Xiaotong Guo, Massachusetts Institute of Technology (MIT)
Anson Stewart, Massachusetts Institute of Technology (MIT)
John Attanucci, Massachusetts Institute of Technology (MIT)

In this paper, a systematic process is proposed for generating, analyzing and visualizing the performance of bus corridors where two or more routes overlap. Measuring performance at the corridor level enables transit planners to identify and prioritize opportunities for performance improvements that cannot be identified through segment, route and network-level analysis alone. This approach also shifts the focus to performance measures that consider the spatial relationship between different routes. First, a map-matching program is used to determine the road sections traversed by each bus route. Corridors are then generated by matching route segments that travel along the same road sections. A classification method for corridors is proposed, where corridors are assigned to different classes depending on whether the segments are identical, if one is the subset of another or if they share the first stop. Finally, performance metrics are aggregated across segments differently depending on the class. Three case studies using actual transit data are used to demonstrate the potential for corridor analysis in planning infrastructure changes, schedule coordination and supply re-allocation. These methods are designed to be generalizable to any transit agency and use only open-source software packages.