Article
Transportation Science & Technology
Lidia Sala, Steve Wright, Caitlin Cottrill, Emilio Flores-Sola
Summary: This paper explores the use of social network data from Twitter to extract reliable demand information for planning commercially viable bus routes to a large music event in Barcelona. By analyzing Twitter influence scores for municipalities in the region, the study successfully identified demand patterns and facilitated the creation of 11 new bus routes that transported over 450 additional passengers from peri-urban and rural areas. This innovative approach demonstrates the potential for social network mining to enhance Mobility Management Planning for large events and improve bus services for rural and peri-urban areas.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Transportation Science & Technology
Yu Gu, Anthony Chen
Summary: This study proposes an advanced equilibrium mode choice model to analyze the mode choice behavior of emerging customized bus (CB) services. The model considers the unique characteristics of CB services, including seat reservation and loyalty scheme. The results demonstrate the importance of considering passenger loyalty and managing mode similarity and heterogeneity when modeling emerging CB services.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Transportation
Jiangbo Wang, Kai Liu, Toshiyuki Yamamoto, De Wang, Guoxu Lu
Summary: This study empirically explores the impact of the built environment on demand-responsive transit (DRT) use through a case study of a successful DRT system in Dalian, China. The results suggest that factors such as residential population, employment density, land use composition, connectivity, and accessibility contribute to DRT use. The findings highlight the potential marketing direction for DRT systems in serving niche markets poorly served by regular transit services.
TRAVEL BEHAVIOUR AND SOCIETY
(2023)
Review
Transportation Science & Technology
Pieter Vansteenwegen, Lissa Melis, Dilay Aktas, Kenneth Sorensen, Fabio Sartori Vieira, Bryan David Galarza Montenegro
Summary: This paper fills the gap in the literature by presenting a comprehensive survey of demand-responsive public bus systems, classifying them based on three degrees of responsiveness: dynamic online, dynamic offline, and static. The paper discusses the optimization problems, data considerations, and testing instance sizes for each system, and identifies potential avenues for future research. Tables are used to structure and summarize the information of all papers.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Green & Sustainable Science & Technology
Fahimeh Golbabaei, Tan Yigitcanlar, Alexander Paz, Jonathan Bunker
Summary: The adoption of autonomous demand-responsive transit can enhance sustainable mobility. This research fills the gap in understanding the socio-demographic characteristics associated with the adoption of autonomous shuttle buses in Australia. The study found that reduced congestion/emissions was seen as the main opportunity, while unreliable technology was viewed as the primary challenge. Fully employed respondents were more familiar with autonomous vehicles, and certain groups, such as males and higher-income individuals, showed more favorable attitudes towards autonomous shuttle buses.
Article
Engineering, Civil
Linghui He, Dongyuan Yang, Jian Li
Summary: Exclusive bus lanes are seen as effective in promoting bus priority but have not increased bus ridership in China. Passenger satisfaction is a key factor in improving the service quality of public transit with bus lanes. Factors such as travel environment, facilities, and convenience have a significant impact on passenger satisfaction, while operational efficiency does not. Choice riders and captive by choice users prefer public transit with bus lanes, while captive users may face financial challenges with private motorized travel. Improvements in crowdedness and driving stability on buses during peak hours could attract more passengers to choose bus travel.
JOURNAL OF ADVANCED TRANSPORTATION
(2021)
Article
Computer Science, Information Systems
Pasqual Marti, Jaume Jordan, Fernando De la Prieta, Holger Billhardt, Vicente Julian
Summary: This paper explores demand-responsive shared transportation as a system that aims to reduce pollution and serve users' displacement needs. Unlike previous works, it proposes a distributed proposal that allows vehicles to retain their private information, and describes a partially dynamic system where vehicles make service decisions based on reported benefits.
Article
Engineering, Civil
Zhaolong Ning, Shouming Sun, MengChu Zhou, Xiping Hu, Xiaojie Wang, Lei Guo, Bin Hu, Ricky Y. K. Kwok
Summary: This paper proposes a framework for joint bus scheduling and route planning to maximize passenger numbers, minimize total route length and required buses, and ensure good user experience. Experimental results show that the proposed algorithms can greatly reduce bus companies' operating costs.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Economics
Giuseppe Inturri, Nadia Giuffrida, Matteo Ignaccolo, Michela Le Pira, Alessandro Pluchino, Andrea Rapisarda, Riccardo D'Angelo
Summary: The study shows that the DRST system is more advantageous than taxis in high demand situations, but its efficiency is limited compared to taxis in low demand and with a small number of vehicles. There is a balance between taxis and the DRST system between high and low demand, where further analysis is needed to identify optimal operational parameters.
Article
Transportation Science & Technology
Bryan David Galarza Montenegro, Kenneth Sorensen, Pieter Vansteenwegen
Summary: Feeder services are discussed in two forms: on-demand service and traditional service. Experimental results show that demand-responsive feeder service demonstrates higher service quality compared to traditional service.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Economics
Stephanie E. Schasche, Robert G. Sposato, Nina Hampl
Summary: This systematic literature review analyzes the research field of demand-responsive transport (DRT) services and identifies the difficulties in the success of these services. The study provides an overview of user-oriented research, detects a threefold conflicting performance expectancy, and discovers a discrepancy between the perception of DRT services and the empirical design of studies. The research points out research gaps regarding performance expectation, user focus, and rurality, and proposes implications for policymakers and practitioners.
Article
Transportation
Jing Zhao, Sicheng Sun, Oded Cats
Summary: This study aims to jointly optimize regular and demand responsive transit (DRT) services by proposing an optimization model to minimize the total travel time of passengers and the total fleet size. A rule-based optimization preparation step is added to reduce the computational load, and a tailored boundary-start-based two-step heuristic algorithm is used to solve the model. The operational level of the DRT significantly affects the travel time of DRT passengers.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2023)
Article
Computer Science, Information Systems
Lei Wang, Lin Zeng, Wanjing Ma, Yuhang Guo
Summary: Customized bus is an alternative public transportation mode that can extend the flexibility and coverage of fixed-route transit networks. Demand control through providing incentives to passengers can attract them to aggregated locations and reduce vehicle detour times. Determining an appropriate incentive scheme for passengers is crucial for improving operation performance.
Article
Economics
Xiaoyun Zhao, Yusak O. Susilo, Anna Pernestal
Summary: Integrating automated buses into public transport can provide more environment-friendly and cost-efficient mobility solutions. User acceptance of the service depends on both innovative technologies and individual behavior changes. To promote continued use, service frequency and comfort need to be enhanced.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2022)
Article
Transportation
Xin Li, Yue Luo, Yanhao Li, Huaiyue Li, Wenbo Fan
Summary: This study proposes a novel demand responsive connector system supported by shared bikes, which can improve the low efficiency issue of traditional DRC systems and reduce total system costs.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2023)
Article
Engineering, Civil
Shasha Liu, Toshiyuki Yamamoto, Enjian Yao
Summary: Research has found that the choices of travel mode and distance are not independent decisions, as individuals usually consider the preferences and needs of other household members. The dependency between mode choice and travel distance is influenced by unobserved factors. Walk mode choice is more dependent on travel distance compared to other travel modes, and the correlation between mode choice and travel distance is higher in complex individual tours. Ignoring this dependency or not considering intra-household interactions may lead to over- or under-estimation of the effects of changes in exogenous variables.
Article
Transportation
Irfan Ullah, Kai Liu, Toshiyuki Yamamoto, Md Shafiullah, Arshad Jamal
Summary: This study predicts the charging time of electric vehicles using machine learning algorithms and optimizes the algorithm parameters to improve accuracy and robustness. The results show that machine learning models based on the gray wolf optimizer perform better in predicting charging time.
TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH
(2023)
Article
Engineering, Civil
Kai Liu, Zhiju Chen, Toshiyuki Yamamoto, Liheng Tuo
Summary: This paper investigates the issue of demand prediction in ride-hailing dispatching and proposes a method based on a convolutional long short-term memory model combined with a hexagonal convolution operation. Experimental analysis using empirical data for Chengdu, China shows that the proposed approach outperforms conventional methods in terms of prediction accuracy. The comparison of 36 spatiotemporal granularities reveals that a hexagonal spatial partition with an 800 m side length and a 30 min time interval achieves the best comprehensive prediction accuracy, although departure demands and arrival demands exhibit different variation trends in prediction errors for various spatiotemporal granularities.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Environmental Studies
Jiangbo Wang, Xinyu (Jason) Cao, Kai Liu, De Wang
Summary: The high failure rate of demand-responsive transit (DRT) systems indicates that DRT services are only feasible in selected areas. However, there have been few studies that quantitatively examine the impact of built environment characteristics on DRT use. This study uses gradient boosting decision trees to analyze the data of customized bus service (CBS, a type of DRT) in Dalian, and investigates the nonlinear relationship between the built environment and CBS use while controlling for demographics and service features. The study finds that local accessibility at the residence and workplace are the most important factors influencing CBS use, followed by the proximity of the workplace to bus stops. Some built environment variables have different impacts on CBS use compared to traditional transit observed in the literature. Additionally, the study identifies threshold associations between built environment variables and CBS use, providing guidance for efficient CBS system design.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2023)
Article
Engineering, Civil
Meilan Jiang, Hitomi Sato, Xiaoshu Diao, Ghasak I. M. A. Mothafer, Toshiyuki Yamamoto
Summary: This study investigates the influence factors of bicycle accidents for different age groups in traffic analysis zones using a multivariate Poisson gamma mixture model. The results show that children are more likely to have accidents in residential areas, while young and adult groups have more accidents in areas with many companies. All age groups are more prone to accidents in areas with many shops. The elderly group is greatly affected by both the number of bicycle trips and spatial spillover effect, making them the most susceptible to bicycle accidents among all age groups.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Transportation
Shasha Liu, Toshiyuki Yamamoto, Toshiyuki Nakamura
Summary: Understanding trends in public transport usage by older people over the years is important for the long-term planning and development of age-friendly public transport. Using six years of smartcard transaction data from Shizuoka, Japan, a latent Markov model is developed to analyze the evolution of public transport usage by older people. The results suggest that age, gender, and residential built environment influence trends in public transport usage by older adults, and there are five latent states for older men and women, respectively, demonstrating the existence of unobserved heterogeneity.
TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH
(2023)
Article
Transportation
Jiangbo Wang, Kai Liu, Toshiyuki Yamamoto, De Wang, Guoxu Lu
Summary: This study empirically explores the impact of the built environment on demand-responsive transit (DRT) use through a case study of a successful DRT system in Dalian, China. The results suggest that factors such as residential population, employment density, land use composition, connectivity, and accessibility contribute to DRT use. The findings highlight the potential marketing direction for DRT systems in serving niche markets poorly served by regular transit services.
TRAVEL BEHAVIOUR AND SOCIETY
(2023)
Article
Engineering, Civil
Irfan Ullah, Kai Liu, Safa Bhar Layeb, Alessandro Severino, Arshad Jamal
Summary: As climate change continues to be a pressing concern, promoting the usage of electric vehicles (EVs) has emerged as a popular response to the pollution caused by fossil-fuel automobiles. Locating and sizing fast-charging stations in existing fuel/gas stations in urban areas can play a crucial role in encouraging people to adopt EVs. This paper presents a model for optimally locating a fast-charging station in an existing gas station in Aichi Prefecture, Japan, taking into consideration real-world constraints, investment cost, and EV users' convenience cost.
JOURNAL OF ADVANCED TRANSPORTATION
(2023)
Article
Chemistry, Multidisciplinary
Yu Wang, Jing Wang, Jialiang Chen, Kai Liu
Summary: This paper proposes an emergency facility location model for the railway dangerous-goods transportation problem. The model is based on an ellipsoidal robust model and introduces a robust control safety parameter to measure the risk preferences of decision makers. It finds the solution for siting emergency facilities when the time and location of emergency events are unknown by limiting the range of uncertain demand, uncertain service, and safety parameters. The model is solved using a genetic algorithm and real data, and a comprehensive analysis of the solution results demonstrates the feasibility and effectiveness of the model under different maximum overcoverages.
APPLIED SCIENCES-BASEL
(2023)
Article
Transportation
Irfan Ullah, Kai Liu, Toshiyuki Yamamoto, Muhammad Zahid, Arshad Jamal
Summary: Growing electric mobility poses challenges to the charging adequacy of electric vehicles (EVs) due to limited charging infrastructure capacities. This study utilizes an interpretable machine learning framework to predict EVs' charging station choice behavior, with the XGBoost model achieving the highest accuracy in prediction. It also employs the newly developed SHAP approach to identify feature importance and the effects of different attributes on charging station choice behavior.
TRAVEL BEHAVIOUR AND SOCIETY
(2023)
Article
Economics
Ning Huan, Toshiyuki Yamamoto, Enjian Yao
Summary: Empirical experience in Europe and China shows that seamless intermodal connections are crucial for expanding the market share of air and high-speed rail (HSR) integration services. However, there are practical obstacles to integrating air and HSR systems. This study proposes a method for improving synchronous operations of air and HSR by unilaterally scheduling airline timetable and airfare. The empirical case study demonstrates that the optimal scheduling solution significantly reduces air-HSR connection time and highlights the importance of efficient shuttle systems, baggage through-check, and flexible ticket services.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2023)
Article
Green & Sustainable Science & Technology
Fei Li, Kai Liu, Jialiang Chen
Summary: This research introduces an approach to address the MNAR-type missing data problem in traffic status prediction by utilizing a multidimensional feature sequence and a second-order hidden Markov model. The proposed method is able to accurately predict traffic status by extracting features and introducing specific matching methods.
Article
Engineering, Civil
Yu Wang, Qixuan Qin, Jialiang Chen, Jiangbo Wang, Kai Liu
Summary: This paper uses AFC data from urban rail transit to extract passenger travel patterns and predicts their destinations using data mining models and MNL models. Furthermore, a two-way search algorithm is developed to find the optimal paths and measure their effectiveness. The proposed method is validated with actual data.
JOURNAL OF ADVANCED TRANSPORTATION
(2023)
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)
