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International Conference on transportation Information and Safety (ICTIS)

作者： Yamin Huo Zhongquan Qiu Yi Xin Shideng Dai Renbin Pan Yi Wen School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Hi-Tech Industrial Development Zone Chengdu China China Academy of Urban Planning and Design Beijing China School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu China Department of Civil and Environmental Engineering University of Tennessee Knoxville TN USA

ISBN: (纸本)9781665497145

Demand-responsive customized bus (DRCB) is a specific type of demand-responsive transit (DRT), which can flexibly customize services according to travel demand. However, few papers explored the influence factors on it. The purpose of this paper is to use the DRCB trip data from November 15 to December 15, 2020, in Xiongan New Area, China, to investigate the influence of built environment, weather, and time factors on DRCB ridership by using a negative binomial regression model. According to the spatiotemporal analysis, DRCB ridership is mainly concentrated in the peak hours of 07:00-09:00 and 17:00-20:00; the spatial distribution is primarily centered in areas such as government service center, railway station, and residential area. The model results show that scenic spot, governmental agency, morning peak period, and evening peak period are positively correlated with the DRCB ridership. Company, medical facility, visibility, rain, and weekend have a negative correlation with DRCB ridership. The results of this paper can provide insights for relevant companies to better understand the demand for DRCB and better plan the DRCB systems.

关键词： Rain Graphical models transportation Companies data models Rail transportation Spatiotemporal phenomena

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A universal opportunity model for human mobility

arXiv

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arXiv 2020年

作者： Liu, Er-Jian Yan, Xiao-Yong Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China Institute of Transportation System Science and Engineering Beijing Jiaotong University Beijing100044 China Compleχ Lab University of Electronic Science and Technology of China Chengdu611731 China

Predicting human mobility between locations has practical applications in transportation science, spatial economics, sociology and many other fields. For more than 100 years, many human mobility prediction models have been proposed, among which the gravity model analogous to Newton's law of gravitation is widely used. Another classical model is the intervening opportunity (IO) model, which indicates that an individual selecting a destination is related to both the destination's opportunities and the intervening opportunities between the origin and the destination. The IO model established from the perspective of individual selection behavior has recently triggered the establishment of many new IO class models. Although these IO class models can achieve accurate prediction at specific spatiotemporal scales, an IO class model that can describe an individual's destination selection behavior at different spatiotemporal scales is still lacking. Here, we develop a universal opportunity model that considers two human behavioral tendencies: one is the exploratory tendency, and the other is the cautious tendency. Our model establishes a new framework in IO class models and covers the classical radiation model and opportunity priority selection model. Furthermore, we use various mobility data to demonstrate our model's predictive ability. The results show that our model can better predict human mobility than previous IO class models. Moreover, this model can help us better understand the underlying mechanism of the individual's destination selection behavior in different types of human mobility. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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An Empirical Guide for Visualization Consistency in Multiple Coordinated Views

An Empirical Guide for Visualization Consistency in Multiple...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Shaocong Tan Chufan Lai Xiaolong Luke Zhang Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) and School of Intelligence Science and Technology Peking University China College of Information Sciences and Technology Pennsylvania State University USA National Engineering Laboratory for Big Data Analysis and Application Peking University China

Visual analytic systems usually provide multiple coordinated views (MCVs) to support data analysis and exploration. Coordination in visual graphics plays an important role in facilitating comprehensive analytical tasks, such as data comparison and cognitive inference. However, individual views in MCVs are probably designed for a specific purpose based on a particular type of data, and insufficient consideration of the intricate relationships among views may lead to inconsistency in visual representation and user interaction across different views. To better understand the inconsistency issues in MCVs and their impacts on user behaviors, this paper reports a study on the analysis and classification of visualization inconsistency based on the reviews of interactive visualization designs and visual analytic systems, and the interviews with stakeholders. We find that inconsistencies are prevalent in MCVs and frequently lead to misleading or even incorrect results. We classify the discovered inconsistencies based on a coordination model of MCVs, and develop an empirical guide for systematic and efficient visualization consistency checking in the design, implementation, and evaluation stage.

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Batch-Based Vehicle Tracking in Smart Cities: A data Fusion and Information Integration Approach

SSRN

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SSRN 2023年

作者： Sun, Zhanbo Huang, Zhihang Hao, Peng Ban, Xuegang Huang, Tianyu School of Transportation and Logistics Southwest Jiaotong University Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China Center for Environmental Research & Technology University of California RiversideCA United States Department of Civil and Environmental Engineering University of Washington SeattleWA United States Civil Aviation Flight University of China Guanghan China

A data fusion and information integration (DFII-VT) framework is proposed to solve the batch-based vehicle matching/tracking problem using heterogeneous fixed-location and mobile sensing data available in smart cities. To make the model more realistic, traffic knowledge such as lane-choice decision, traffic merging, travel time and vehicle characteristics are calibrated using the historical dataset and then integrated into the model. The problem can be formulated as a combinatorial optimization model, and solved using a dynamic programming/Kuhn-Munkres algorithm-based two-step approach. By doing so, the proposed method can obtain individual travel times of the matched data pairs, and these can be directly used to estimate the corridor travel times of individual vehicles. The experimental results show that the fusion of mobile sensing data and fixed-location data yields significantly better results than using single-source data. Significant improvements in matching accuracy are obtained as more traffic information is integrated into the model. Therefore, the method may be considered a framework for integrating the manifold traffic information acquired within smart cities to obtain more accurate matching results and further used to optimize other fine-grained traffic applications, such as estimating vehicle trajectories along arterial corridors, estimating individual vehicle-based fuel consumption/emissions, and helping to infer real-time queuing processes at signalized intersections. The paper also studies some practical issues related to the use of heterogeneous traffic data, such as data errors, and detection failures. © 2023, The Authors. All rights reserved.

关键词： Travel time

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Shared Autonomous Vehicle Integrated with Public Transit: Service Characteristics and System Design Model

SSRN

引用

SSRN 2023年

作者： Shi, Ziyi Luo, Sida Niu, Luyao Shao, Chunfu Institute of Intelligent Transportation Systems College of Civil Engineering and Architecture Zhejiang University Hangzhou310058 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University #3 Shangyuancun Haidian District Beijing100044 China

Promoting shared autonomous vehicles (SAVs) in tourist cities is promising, but probably induces so-called "round trips" — Travelers start and back to the same location after a tour for entertainment in SAV (e.g. sightseeing and photography) due to lack of drivers and better privacy. To serve different types of demand including round trips, this study proposes a mobility system called the Shared Autonomous Vehicle and Transit (SAV-T) integrated system, which strikes a balance between accessibility and efficiency through feeding fixed-route transit (FRT) by SAV. Service characteristics of SAVs in the proposed system are devised: Some SAVs connect passengers to FRT stops who travel across different zones;another portion of SAVs serves passengers whose trips are within the zone;the rest serves round trips. SAVs serving round trips prohibit carpool mainly to enhance privacy while allowing carpool as they serve normal trips. To overcome the difficulty to determine SAV fleet size required to accommodate different types of demand, we derive flow equilibrium equations using network queuing theory. The fleet size contributes to the system cost function, which is the objective of SAV-T system design program that is formulated as a mixed integer program by parsimonious continuum modeling and solved numerically. We find that despite serving round trips that allow no carpooling, agency costs and system costs of SAV-T are still greatly saved compared to the FRT-only system (up to 25% saving in low-demand cities). Also, cities with higher trip density can allow a larger proportion of round trips when a target system cost is given. © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Capacity analysis of ship-tugging operations in a large container port

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Asian Transport Studies 2020年 6卷

作者： Kang, Liujiang Gao, Song Meng, Qiang Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University 100044 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan 430063 China Department of Civil and Environmental Engineering National University of Singapore Singapore 117576 Singapore

In this study, we deal with the simulation problem of ship-tugging operations in a large container port. First, we build a tugboat-service network using a directed graph, where the nodes consist of tugboat bases, anchorages, and berths while the arcs represent the directed links between nodes. Second, we develop a marine traffic simulation system, which can simulate maritime traffic conditions in restricted waters, such as large container ports and busy straits. The marine traffic simulation system is a customized system. Users are allowed to set their study areas and virtual traffic environment (such as traffic demand and network structure) at will. Finally, we conduct various simulations for a real tugboat-service network of Singapore Port, which includes one anchorage, one tugboat base, two berths, and two channels between the anchorage and berths. A detailed analysis of the tugging capacity is provided based on simulation results. © 2020 The Authors

关键词： Capacity estimation Container ports Simulation system development Tugging operations

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Continual Learning for Smart City: A Survey

arXiv

引用

arXiv 2024年

作者： Yang, Li Luo, Zhipeng Zhang, Shiming Teng, Fei Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Sichuan Chengdu611756 China

With the digitization of modern cities, large data volumes and powerful computational resources facilitate the rapid update of intelligent models deployed in smart cities. Continual learning (CL) is a novel machine learning paradigm that constantly updates models to adapt to changing environments, where the learning tasks, data, and distributions can vary over time. Our survey provides a comprehensive review of continual learning methods that are widely used in smart city development. The content consists of three parts: 1) Methodology-wise. We categorize a large number of basic CL methods and advanced CL frameworks in combination with other learning paradigms including graph learning, spatial-temporal learning, multi-modal learning, and federated learning. 2) application-wise. We present numerous CL applications covering transportation, environment, public health, safety, networks, and associated datasets related to urban computing. 3) Challenges. We discuss current problems and challenges and envision several promising research directions. We believe this survey can help relevant researchers quickly familiarize themselves with the current state of continual learning research used in smart city development and direct them to future research trends. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart city

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Intra-Person Camera Adversarial for Intra-Camera Supervised Person Re-Identification

Intra-Person Camera Adversarial for Intra-Camera Supervised ...

引用

International Conference on Automatic Face and Gesture Recognition

作者： Ruochen Tang Xun Gong School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Re-identification (ReID) of individuals across different cameras is a challenging task due to the high-quality large-scale datasets required for the model. Intra-camera supervised (ICS) person re-identification has been proposed to address the high cost of annotating large-scale datasets, but reducing the gap between camera domains remains a major challenge. Current approaches use intra- and inter-camera learning with contrastive learning performed separately in both phases. However, the effect of features from the same person under different cameras on the model during inter-camera learning, and the fine-grained characteristic where the same person can be classified into multiple classes based on the camera labels, still require further research. To address this issue, we propose a Camera-Based Contrastive Learning (CBCL) method that moves features away from their respective cameras and closer to other cameras to reduce domain gaps. We also introduce an Intra-Person Camera Adversarial (IPCA) loss that effectively utilizes fine-grained characteristics of person re-identification and improve IPCA by introducing camera labels to obtain IPCA_2 which achieves better model recognition performance than IPCA alone. Extensive experiments on multiple datasets demonstrate that our method outperforms existing methods and is comparable to fully-supervised methods.

关键词： Costs Scalability Face recognition Contrastive learning Gesture recognition Cameras Robustness

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On the precise quantification of the impact of a single discretionary lane change on surrounding traffic

arXiv

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arXiv 2024年

作者： Hou, Kangning Zou, Jia Zheng, Fangfang Liu, Xiaobo He, Zhengbing School of Transportation and Logistics Southwest Jiaotong University Western Hi-tech Zone Chengdu Sichuan611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Western Hi-tech Zone Chengdu Sichuan611756 China Senseable City Lab Massachusettes Institute of Technology CambridgeMA02421 United States

Lane-changing is a critical maneuver of vehicle driving, and a comprehensive understanding of its impact on traffic is essential for effective traffic management and optimization. Unfortunately, existing studies fail to adequately distinguish the impact of lane changes from those resulting from natural traffic dynamics. Additionally, there is a lack of precise methods for measuring the spatial extent and duration of the impact of a single discretionary lane change, as well as a definitive metric to quantify the overall spatiotemporal impact. To address these gaps, this study introduces a quantitative indicator called the Corrected Travel Distance Bias (CTDB), which accounts for variable speeds due to inherent traffic dynamics, providing a more accurate assessment of lane-changing impacts. A comprehensive methodology is developed to compare vehicle trajectory data before and after lane-changing events, measuring both the magnitude and spatiotemporal extent of the lane-changing impact. The results, based on the Zen traffic data from Japan, indicate that the impact of a lane change in the target lane lasts an average of 23.8 seconds, affecting approximately 5.6 vehicles, with a CTDB value of -10.8 meters. In contrast, in the original lane, the impact lasts 25 seconds, affects 5.3 vehicles, and yields a CTDB value of 4.7 meters. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicles

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Spatial Analysis of Taxi Speeding Event Using GPS Trajectory data *

Spatial Analysis of Taxi Speeding Event Using GPS Trajectory...

引用

International Conference on Intelligent transportation

作者： Chuanyun Fu Yue Zhou Chuan Xu Haipeng Cui School of Transportation and Logistics Southwest Jiaotong University Southwest Jiaotong University Chengdu P.R. of China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu P.R. of China School of Transportation and Logistics Southwest Jiaotong University Chengdu P.R. of China College of Transportation Engineering Tongji University Shanghai P.R. of China

ISBN: (纸本)9781538670255

GPS-equipped taxis can generate large-scale trajectory data which enables scholars to uncover taxi abnormal behaviors, such as speeding. Hence, this study intends to disclose the characteristics of taxi speeding event (SE), identify the spatial factors impacting the frequency of taxi SE, using 5,757 taxis' GPS trajectory data in a part of the central area of Chengdu city from November 1 to November 30, 2016, along with speed limit data, electronic map data, etc. By comparing the calculated travel speeds with speed limits, taxi SEs were identified and categorized into five groups: SE (

关键词： Public transportation Global Positioning System Roads Trajectory Correlation Vehicles

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