Article
Economics
Benjamin J. Tomhave, Alireza Khani
Summary: This study proposes a new method for estimating transit route choice, which generates high-quality transit path choice sets and produces detailed temporal information. By estimating a multinomial logit model, the most likely transit path can be calculated, and it is found that express bus routes have a negative impact on low-income groups while transitways have a positive impact on high-income groups.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2022)
Article
Engineering, Civil
Yohee Han, Youngchan Kim, Jisun Ku, Yeonghun Jung, Jeongrae Roh
Summary: This study focused on designing a map matching algorithm for processing large volumes of GPS data collected, which significantly reduced processing time when applied to real-time data processing systems. The findings are expected to have a positive impact in future traffic operations and management.
KSCE JOURNAL OF CIVIL ENGINEERING
(2021)
Article
Economics
Andres Sevtsuk, Rounaq Basu
Summary: This paper examines the influence of path length and turns on pedestrian route choice. The findings indicate that turns are dependent on the spatial properties of street networks, while distance has a consistently larger effect. Only in specific street networks can turns alone explain route choice behavior. Therefore, in considering route selection, turns and other environmental qualities of a route should be taken into account in addition to distance.
JOURNAL OF TRANSPORT GEOGRAPHY
(2022)
Article
Transportation
Andres Sevtsuk, Rounaq Basu, Xiaojiang Li, Raul Kalvo
Summary: This study analyzes pedestrian route choice preferences in San Francisco, California using big data from smartphone applications. Various street attributes impacting pedestrian route choice are studied, with innovations including generating alternative paths for route choice estimation and gathering route attribute information from Google Street View images. The estimated coefficients can be operationalized for policy and planning to improve pedestrian accessibility to BART stations in San Francisco.
TRAVEL BEHAVIOUR AND SOCIETY
(2021)
Article
Economics
Chieh Hsueh, Jen-Jia Lin
Summary: This study used GPS records of shared electric scooter users in Taipei, Taiwan, to analyze the factors influencing their route choices. The results revealed that scooter riders are willing to prioritize safety, even if it means traveling longer distances or spending more time and money. The study also found that the preferences for route choices vary depending on the time of day, with riders prioritizing speed during the morning peak period and traffic smoothness during off-peak periods. The research also discovered the positive influence of right turn density on route choices, which differs from previous studies on car and bicycle users. The findings of this study contribute to existing literature on route choices and have practical implications for improving scooter navigation services and creating scooter-friendly environments.
JOURNAL OF TRANSPORT GEOGRAPHY
(2023)
Article
Economics
Darren M. Scott, Wei Lu, Matthew J. Brown
Summary: This research collected and analyzed route choice data of bike share users to find that users prefer straighter routes with fewer turns, and are willing to detour for better bicycle facilities.
JOURNAL OF TRANSPORT GEOGRAPHY
(2021)
Article
Engineering, Civil
Qi Cao, Gang Ren, Dawei Li, Haojie Li, Jiangshan Ma
Summary: Existing map matching methods developed for GPS data face limitations due to small sample sizes and position errors. This study introduces AVI-MM, a customized map matching method for sparse AVI data, utilizing decomposition of trajectories and candidate sub-path generation to enhance matching accuracy. By defining matching probability based on spatial-temporal analysis and route choice behavior, AVI-MM offers improved robustness and accuracy for identifying links.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Economics
Rounaq Basu, Andres Sevtsuk
Summary: This study uses big data to analyze pedestrian route choice behavior in Boston, exploring preferences for route attributes. The findings can inform walkability policy and practice, with recommendations for future research to focus on hard-to-reach populations.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2022)
Article
Engineering, Electrical & Electronic
Yingbing Li, Yan Zhang, Min Chen
Summary: This article presents a new algorithm for reconstructing vehicle trajectories from sparse and noisy fingerprint signals, with practical application in a high-speed toll collection system. The algorithm matches communication base station identification numbers with a special radiomap to generate complete driving routes.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Civil
Songyi Zhang, Runsheng Wang, Zhiqiang Jian, Wei Zhan, Nanning Zheng, Masayoshi Tomizuka
Summary: High-definition (HD) map is crucial for autonomous driving, providing accurate and rich geometric and semantic information for modules such as behavior prediction and motion planning. However, the current scalability and computational efficiency of HD map generation cannot meet the needs of highly automated driving. This paper proposes a fast and robust path reconstruction method that compresses dense reference line points into sparse parameters without significant information loss. Experimental results demonstrate that the proposed method generates more accurate path reconstruction with significantly reduced computational time compared to existing methods.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Transportation
Yuanfang Zhu, Meilan Jiang, Toshiyuki Yamamoto
Summary: The proposed method aims to improve the accuracy and efficiency of the local map-matching algorithm without affecting its efficiency by using an incremental map-matching algorithm, identifying mismatching links, and correcting error links. Experimental results show that the proposed method significantly increases the accuracy and efficiency of map-matching, outperforming benchmark global map-matching algorithms in terms of error rate and computation time.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Dongqing Zhang, Yucheng Dong, Zhaoxia Guo
Summary: The paper introduces a novel turning point-based offline map matching algorithm, which improves matching accuracy and efficiency by segmenting the entire trajectory into sub-trajectories and selecting the best-matched path from the K-shortest paths. Extensive experiments show that the algorithm outperforms five benchmark algorithms in terms of correctly matched percentages, incorrectly matched percentages, and matching speeds.
INFORMATION SCIENCES
(2021)
Article
Transportation
Zhenxing Yao, Yanchen Wang, Fei Yang, Yang Cheng, Bin Ran
Summary: This paper proposes a method to identify travel routes using handoff trajectory data from mobile phone networks, providing valuable data for transportation planning projects at low cost. By using the Earth Mover's Distance algorithm to identify travel routes, the proposed method is much more efficient than classical sequence similarity algorithms and can accurately detect small spacing parallel roads.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Economics
Dawei Li, Siqi Feng, Yuchen Song, Xinjun Lai, Shlomo Bekhor
Summary: This paper discusses a set of multinomial models with asymmetric choice probability functions to tackle the class imbalance issue in mode choice. It proposes three approaches to parameterize the shape parameter for route choice and tests the models using two independent GPS datasets. The results show the existence of class imbalance in both cases and the performance of the asymmetric models is dependent on the context, with different parameterizations providing different interpretations of the asymmetry in route choice.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2023)
Article
Chemistry, Analytical
Kyuho Lee, Hyohyuk Choi, Junghun Kim
Summary: This research developed a dual GPS antenna-based autonomous driving system and path tracking algorithm for a robot combine harvester. The system and algorithm were verified through experiments and showed an efficiency of 76.7% during harvesting work.
Article
Engineering, Civil
Nicholas Molyneaux, Riccardo Scarinci, Michel Bierlaire
Summary: This paper investigates how to improve the level-of-service experienced by pedestrians by regulating and controlling their movements with a dynamic traffic management system, and emphasizes the lack of attention in literature to dynamic traffic management systems for pedestrian flows.
Article
Economics
Meritxell Pacheco Paneque, Michel Bierlaire, Bernard Gendron, Shadi Sharif Azadeh
Summary: The study highlights the mismatch between design and planning decisions and demand in transportation systems, emphasizing the importance of allowing for interactions between the two. Discrete choice models (DCM) provide a way to capture the impact of decisions on user behavior, but the non-linear and non-convex nature of demand expressions derived from DCM restricts their integration in optimization problems.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Economics
Omar Abou Kasm, Ali Diabat, Michel Bierlaire
Summary: This paper addresses the vessel scheduling problem with pilotage and tugging constraints at seaports. A mixed integer programming formulation and an exact solution approach are proposed to solve the problem. The proposed model is compared with the traditional first-come first-serve policy in vessel scheduling, showing significant improvements during congestion periods. Computational study demonstrates the capability of the proposed solution approach in solving real-size cases in a reasonable time.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2021)
Article
Engineering, Civil
Stefano Bortolomiol, Virginie Lurkin, Michel Bierlaire
Summary: The study proposes a framework for finding optimal price-based policies in markets with oligopolistic competition. It models consumers as utility maximizers using random utility theory, while suppliers are modeled as profit maximizers. Market competition is modeled as a non-cooperative game, and the regulator can influence behavior through subsidies or taxes.
Article
Operations Research & Management Science
Stefano Bortolomiol, Virginie Lurkin, Michel Bierlaire
Summary: This paper introduces a framework to find approximate equilibrium solutions of oligopolistic markets in transportation, utilizing discrete choice models and algorithmic approach. The methodology successfully approximates equilibrium solutions for transportation case studies featuring complex models and heterogeneous demand.
TRANSPORTATION SCIENCE
(2021)
Article
Economics
Nicola Ortelli, Tim Hillel, Francisco C. Pereira, Matthieu de Lapparent, Michel Bierlaire
Summary: Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors due to exponential growth in possible specifications with the number of variables. This paper proposes an algorithm that translates the task into a multi-objective combinatorial optimization problem and uses a variant of the variable neighborhood search algorithm to generate promising model specifications. The algorithm proves to effectively assist modelers in developing interpretable and powerful models by providing relevant insights in reasonable amounts of time.
JOURNAL OF CHOICE MODELLING
(2021)
Article
Transportation Science & Technology
Julien Haan, Laurie A. Garrow, Aude Marzuoli, Satadru Roy, Michel Bierlaire
Summary: This study calculates the air taxi commuter demand for the 40 most populous combined statistical areas (CSAs) in the U.S. using cell phone data, census data, and a mode choice model. The demand is concentrated in a few CSAs, with New York City, Los Angeles, and Washington, D.C. generating 33 percent of the overall air taxi demand.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Transportation Science & Technology
S. Binder, M. Y. Maknoon, Sh Sharif Azadeh, M. Bierlaire
Summary: This study presents a passenger-centric approach for timetable rescheduling in case of railway disruptions, using a multi-objective algorithm to find high-quality solutions efficiently on Swiss and Dutch railway networks.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Economics
Rico Krueger, Michel Bierlaire, Ricardo A. Daziano, Taha H. Rashidi, Prateek Bansal
Summary: The study evaluates the ability of mixed logit models with unobserved inter-and intra-individual heterogeneity to accurately predict individual choice behavior. Results show that these models do not significantly improve choice prediction accuracy over standard mixed logit models, even in scenarios with high levels of intra-individual taste variation. Additionally, estimation of mixed logit with unobserved heterogeneity requires significantly more computation time than standard mixed logit.
JOURNAL OF CHOICE MODELLING
(2021)
Article
Engineering, Civil
Filipe Rodrigues, Nicola Ortelli, Michel Bierlaire, Francisco Camara Pereira
Summary: This paper introduces a method that utilizes the Bayesian framework and automatic relevance determination to automatically determine the optimal utility function specification from a large amount of data. Experimental results show that the proposed method can accurately recover the true specifications and discover high-quality specifications in real choice data.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Economics
Janody Pougala, Tim Hillel, Michel Bierlaire
Summary: This paper proposes a new modeling approach for daily activity scheduling, integrating different dimensions of scheduling choices into a single optimization problem. By capturing the trade-offs between scheduling decisions for multiple activities, the proposed framework can generate complex and realistic distributions of starting time and duration for different activities.
JOURNAL OF CHOICE MODELLING
(2022)
Article
Economics
Sh. Sharif Azadeh, Bilge Atasoy, Moshe E. Ben-Akiva, M. Bierlaire, M. Y. Maknoon
Summary: This paper introduces a new mathematical model called a choice-driven dial-a-ride problem (CD-DARP) for operational planning of urban mobility services. By integrating with choice models and assortment optimization, a solution is proposed that outperforms dynamic DARP in efficiently reducing routing costs and improving the number of customers served.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2022)
Article
Computer Science, Interdisciplinary Applications
Meritxell Pacheco Paneque, Bernard Gendron, Shadi Sharif Azadeh, Michel Bierlaire
Summary: This paper proposes a novel Lagrangian decomposition method for solving choice-based optimization problems. By generating upper and lower bounds for the original problem and calculating the duality gap, the quality of the solutions can be assessed. Experimental results show that the decomposition method provides high-quality solutions within short computational times.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Michel Bierlaire, Edoardo Fadda, Lohic Fotio Tiotsop, Daniele Manerba
Summary: Social engagement is a business model that transforms service users into active participants. By modeling the behavior of contacted candidates and using concepts from utility theory, a chance-constrained optimization model is proposed to minimize costs. The proposed solution approach and its computational efficiency are investigated through experiments.
PROCEEDINGS OF THE 2022 17TH CONFERENCE ON COMPUTER SCIENCE AND INTELLIGENCE SYSTEMS (FEDCSIS)
(2022)
Review
Operations Research & Management Science
Selin Atac, Nikola Obrenovic, Michel Bierlaire
Summary: Despite using different vehicle types, different vehicle sharing systems face similar management challenges and optimization problems; a generalized and holistic VSS management framework has been created to be applicable to any vehicle type; framework components, their relationships, and tasks have been identified through systematic literature review, identifying gaps and research avenues.
EURO JOURNAL ON TRANSPORTATION AND LOGISTICS
(2021)
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)
