Article
Engineering, Civil
Malte Bieler, Anders Skretting, Philippe Budinger, Tor-Morten Gronli
Summary: Public transportation can be made more attractive and efficient by implementing seamless and automated fare collection solutions using mobile devices. This paper provides a comprehensive review of existing technologies, predictive behavior models, and the use of machine learning to create valuable business intelligence. The implementation of automated fare collection systems is expected to have a significant positive impact on customer experiences, the emergence of new business models, and the reduction of pollutant emissions in urban transportation.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Economics
Gian-Claudia Sciara, Mashrur Rahman, Rydell Walthall
Summary: Since the early 1990s, U.S. metropolitan planning organizations (MPOs) have gained more authority in shaping regional transportation spending, with recent federal policies slightly increasing formal transit involvement in investment decisions. However, concerns remain that public transit operators and needs are underrepresented in decision-making. The study reveals disparities in transit's influence from region to region based on different forms of board representation for transit.
Article
Economics
Qianwen Guo, Yanshuo Sun, Paul Schonfeld, Zhongfei Li
Summary: This paper presents a microeconomic model for designing time-dependent transit pricing schemes considering elastic and spatiotemporally distributed demand. The model optimizes fares, headway, vehicle capacity, and fleet size to maximize social welfare.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2021)
Article
Economics
Caio Pieroni, Mariana Giannotti, Bianca B. Alves, Renato Arbex
Summary: This study analyzed the temporal and spatial patterns of urban transit movements in precarious settlement areas in Sao Paulo, Brazil using smart card data mining. The results revealed differences in travel behavior between low-income residents from precarious settlements and middle/high-income-class residents, with a focus on identifying low-paid employment travel patterns. The empirical evidence highlights smart card data's potential in uncovering low-paid employment spatial and temporal patterns.
JOURNAL OF TRANSPORT GEOGRAPHY
(2021)
Article
Chemistry, Analytical
Kiarash Ghasemlou, Murat Ergun, Nima Dadashzadeh
Summary: This study evaluates smart card data from Kocaeli, Turkey to show the disparity in actual demand characteristics and user shares between daily and monthly data, highlighting the limitations of existing trip-based public transport planning methods. The analysis reveals that daily travel data without information on day-to-day trip frequency and public transport use can lead to incorrect estimations of actual demand, emphasizing the importance of a user-based approach for more realistic transportation planning and investment prioritization.
Article
Operations Research & Management Science
Daniel F. Silva, Alexander Vinel, Bekircan Kirkici
Summary: With advancements in mobile technology, public transit agencies are exploring on-demand public transit. This study introduces models to evaluate system performance and predict performance metrics probability distribution, aiding in capital planning and decision-making.
TRANSPORTATION SCIENCE
(2022)
Article
Engineering, Civil
Rudi Alexander Rendel, Chris Bachmann
Summary: This research presents a method to estimate the societal benefit of substituting private vehicle trips with public transportation trips. The external costs of private and public transportation were assessed using a travel demand model, and a shift from private to public mode was simulated. The results demonstrated that the economic benefit of increased transit ridership outweighed the financial cost of a new bus, suggesting the need for subsidies to enhance ridership and mitigate externalities.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Transportation
Yi Yang, Xinguo Jiang, Yusong Yan, Tao Liu, Yu Jiang
Summary: This study develops a new optimization model to design a bimodal transit system that maximizes the profit of a transit agency considering demand elasticity and fare structures. The study finds that increasing elasticity parameters lead to a decrease in net profit. Additionally, a distance-based fare scheme results in the least actual demand but generates the most profit. Lastly, passengers prefer a rail-bus system over a BRT-bus system, especially at higher demand levels.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2023)
Article
Engineering, Civil
Dwayne Marshall Baker, Orly Linovski
Summary: This research used agglomerative hierarchical clustering and propensity score matching to study the impact of BRT on transit ridership change in Winnipeg. The results showed that stop clusters directly along the BRT corridor did not lead to an increase in transit ridership, while routes connecting to BRT stations did experience an increase in ridership.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Engineering, Civil
Xia Zhao, Yong Zhang, Yongli Hu, Shun Wang, Yunhui Li, Sean Qian, Baocai Yin
Summary: To address the lack of existing visualization techniques in passenger mobility correlations, a visual analytical system is provided for exploring group-based and individual-based mobility correlations of interest, which is further examined based on their spatiotemporal distributions in trajectories and ODs.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Information Systems
Juan Benavente, Borja Alonso, Andres Rodriguez, Jose Luis Moura
Summary: This study presents a flexible methodology for defining and improving each distinct trip in a transportation system by integrating multiple data sources and criteria. Through a case study, the data correction and completion methods can reduce data distortions, providing a reliable foundation for further analysis.
Article
Energy & Fuels
K. Purnell, A. G. Bruce, I. MacGill
Summary: Battery electrified public transit (BEPT) is a promising solution to reduce transport emissions and air pollution, but it poses challenges to the low-voltage distribution network. This paper presents a tool for estimating the energy and charging demand of BEPT using public data and demonstrates it through case studies. The impact of BEPT on the low-voltage network is significant, increasing peak demand and exacerbating evening peak demand in the case of New South Wales.
Article
Engineering, Electrical & Electronic
Xia Zhao, Yong Zhang, Yongli Hu, Zhen (Sean) Qian, Hao Liu, Baocai Yin
Summary: This research utilizes smart card data generated by transit riders in Beijing in August 2015 to explore passengers' relation proximity based on a weighted relation strength model. The experimental results confirm the method's robustness and precision in inferring passengers' relation proximity.
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Computer Science, Information Systems
Xinyu Lu, Jie Li, Chentao Wu, Jinsong Wu, Mahmoud Daneshmand
Summary: This article proposes a novel method to mine similarity information of passengers by leveraging passengers' communication behaviors hidden in subway card usage data. By organizing passengers as a graph and using node embedding and cosine similarity, the proposed method can effectively improve the accuracy of similarity measurement and provide social features that are distinguishable from travel behavior patterns.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Environmental Studies
Xia Zhao, Mengying Cui, David Levinson
Summary: This study investigates the temporal variability in travel patterns of over 3.3 million passengers in public transit in Beijing over 120 days. The results show that commuters and non-commuters exhibit different patterns in terms of travel distance and coverage, and commuters are less affected by the day of the week. Moreover, travel distance and frequency increase faster and more linearly than space-coverage features.
ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE
(2023)
Article
Management
Minh Hoang Ha, Hoa Nguyen Phuong, Huyen Tran Ngoc Nhat, Andre Langevin, Martin Trepanier
Summary: This article studies the clustered traveling salesman problem with a prespecified order on the clusters. In this problem, delivery locations are divided into clusters with different urgency levels, and more urgent locations must be visited first. However, this may result in an inefficient route in terms of traveling cost. The article proposes two approaches to solve this problem and demonstrates their effectiveness through experiments.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2022)
Article
Engineering, Civil
Cen Zhang, Jan-Dirk Schmocker, Martin Trepanier
Summary: The study proposes a new model based on Markov Chains to predict the monthly usage frequency of members in a car-sharing scheme. By including five latent user 'life stages' and validating the model on panel data, the effectiveness of the model in predicting car-sharing usage frequency is demonstrated. This approach is effective for predicting usage in novel transport schemes.
Article
Computer Science, Artificial Intelligence
Kevin Pasini, Mostepha Khouadjia, Allou Same, Martin Trepanier, Latifa Oukhellou
Summary: The study aims to detect the impact of disturbances on a transportation network through smart card data analysis, focusing on contextual anomaly detection using machine learning models to build a robust anomaly score. The research highlights the importance of variance normalization on prediction residuals under a dynamic context, showing the relevance in building a reliable anomaly score.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Chemistry, Analytical
Aliasghar Mehdizadeh Dastjerdi, Catherine Morency
Summary: This study focuses on short-term demand prediction for bike-sharing services in Montreal using a deep learning approach. Results show that deep learning models outperform traditional ARIMA models, and the addition of extra input features improves prediction accuracy.
Article
Engineering, Civil
Jerome Laviolette, Catherine Morency, Owen D. Waygood, Konstadinos G. Goulias
Summary: This paper studies the spatial dependencies of household car ownership rates in the Island of Montreal and finds that sociodemographic and built environment variables have a significant impact on car ownership rates, with failure to control for spatial dependence resulting in an overestimation of the direct influence of built environment variables.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Economics
Elodie Deschaintres, Catherine Morency, Martin Trepanier
Summary: This paper evaluates the value of traditional surveys and emerging data in cross-analyzing the temporal variability of travel behaviors. The results show that day-to-day variability in travel behavior can be accurately inferred from a single-day survey, and demonstrate the complementarity and potential combination of the two data sources.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2022)
Article
Transportation Science & Technology
Remi Decouvelaere, Martin Trepanier, Bruno Agard
Summary: This study presents a spatiotemporal clustering tool that allows for adjusting the importance of space and time. By testing different parameter values, it is found that the influence of space and time can be controlled and the obtained clusters vary depending on whether one or both dimensions are considered.
Article
Engineering, Civil
Mohamed Khachman, Catherine Morency, Francesco Ciari
Summary: Research has found that traditional spatialized population synthesis methods often result in inconsistencies between population data and the built environment, and have issues with transferability. Therefore, a new integrated multiresolution framework (IMF) has been proposed, which achieves better spatial precision and overall quality while maintaining the accuracy of synthetic populations.
Article
Public, Environmental & Occupational Health
Camille Garnier, Martin Trepanier, Catherine Morency
Summary: The research aims to estimate the number of people in Quebec eligible for paratransit services and to estimate the latent demand for the STM paratransit service in Montreal. A filter algorithm is used to determine eligibility and compare it with current service usage, revealing a significant latent eligible population.
JOURNAL OF TRANSPORT & HEALTH
(2022)
Article
Chemistry, Analytical
Miratul Khusna Mufida, Abdessamad Ait El Cadi, Thierry Delot, Martin Trepanier, Dorsaf Zekri
Summary: This study addresses the challenge of developing accurate and efficient parking occupancy forecasting models for autonomous vehicles at the city level. A novel two-step clustering technique is proposed to group parking lots based on their spatiotemporal patterns, allowing for the development of accurate occupancy forecasting models for a set of parking lots. Real-time parking data was used to build and evaluate the models, demonstrating the effectiveness of the proposed strategy in reducing model deployment costs and improving model applicability and transfer learning across parking lots.
Article
Engineering, Civil
Nazmul Arefin Khan, Catherine Morency
Summary: The COVID-19 pandemic in 2020 has significantly changed daily mobility patterns worldwide. A web-based survey was conducted in Montreal, Canada in April-May 2020 to capture the impacts of the pandemic on travel behaviors. Using data from this survey, this paper proposes insights into how people are planning to travel in a post-COVID-19 world. The study identifies two latent segments, suburbanite and urbanite people, and finds considerable heterogeneity across sample individuals.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Engineering, Civil
Hubert Verreault, Catherine Morency
Summary: Most transportation organizations have collected a large amount of data through travel surveys, which is used for transportation planning and modeling. However, relying on outdated data is becoming problematic, especially for less populous areas. This paper proposes a methodology to combine different survey samples to better utilize historical travel survey data.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Operations Research & Management Science
Xiaoxu Chen, Zhanhong Cheng, Jian Gang Jin, Martin Trepanier, Lijun Sun
Summary: This paper proposes a Bayesian probabilistic model for forecasting bus travel time and estimated time of arrival (ETA). The model can capture interactions between buses, handle missing values, and depict the multimodality in bus travel time distributions. Using time-varying mixing coefficients, it can infer systematic temporal variations in bus operation.
TRANSPORTATION SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zhanhong Cheng, Xudong Wang, Xinyuan Chen, Martin Trepanier, Lijun Sun
Summary: This study explores the relationship between vehicle speed and density on the road, highlighting biases in calibrating single-regime speed-density models using least-squares method. By modeling the covariance of residuals with zero-mean Gaussian Process, a new calibration method is proposed that significantly reduces biases, achieves similar effects as the weighted least-squares method, functions as a non-parametric speed-density model, and provides a Bayesian solution for estimating posterior distributions.
IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Transportation Science & Technology
Yue Zhao, Liujiang Kang, Huijun Sun, Jianjun Wu, Nsabimana Buhigiro
Summary: This study proposes a 2-population 3-strategy evolutionary game model to address the issue of subway network operation extension. The analysis reveals that the rule of maximum total fitness ensures the priority of evolutionary equilibrium strategies, and proper adjustment minutes can enhance the effectiveness of operation extension.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Hongtao Hu, Jiao Mob, Lu Zhen
Summary: This study investigates the challenges of daily storage yard management in marine container terminals considering delayed transshipment of containers. A mixed-integer linear programming model is proposed to minimize various costs associated with transportation and yard management. The improved Benders decomposition algorithm is applied to solve the problem effectively and efficiently.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Zhandong Xu, Yiyang Peng, Guoyuan Li, Anthony Chen, Xiaobo Liu
Summary: This paper studied the impact of range anxiety among electric vehicle drivers on traffic assignment. Two types of range-constrained traffic assignment problems were defined based on discrete or continuous distributed range anxiety. Models and algorithms were proposed to solve the two types of problems. Experimental results showed the superiority of the proposed algorithm and revealed that drivers with heightened range anxiety may cause severe congestion.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Chuanjia Li, Maosi Geng, Yong Chen, Zeen Cai, Zheng Zhu, Xiqun (Michael) Chen
Summary: Understanding spatial-temporal stochasticity in shared mobility is crucial, and this study introduces the Bi-STTNP prediction model that provides probabilistic predictions and uncertainty estimations for ride-sourcing demand, outperforming conventional deep learning methods. The model captures the multivariate spatial-temporal Gaussian distribution of demand and offers comprehensive uncertainty representations.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Benjamin Coifman, Lizhe Li
Summary: This paper develops a partial trajectory method for aligning views from successive fixed cameras in order to ensure high fidelity with the actual vehicle movements. The method operates on the output of vehicle tracking to provide direct feedback and improve alignment quality. Experimental results show that this method can enhance accuracy and increase the number of vehicles in the dataset.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Mohsen Dastpak, Fausto Errico, Ola Jabali, Federico Malucelli
Summary: This article discusses the problem of an Electric Vehicle (EV) finding the shortest route from an origin to a destination and proposes a problem model that considers the occupancy indicator information of charging stations. A Markov Decision Process formulation is presented to optimize the EV routing and charging policy. A reoptimization algorithm is developed to establish the sequence of charging station visits and charging amounts based on system updates. Results from a comprehensive computational study show that the proposed method significantly reduces waiting times and total trip duration.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
