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Zhiyuan Liu
The Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, People's Republic of China

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Journal article
Published: 02 August 2021 in Transportation Research Part E: Logistics and Transportation Review
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This study proposes an improved learning-and-optimization train fare design method to deal with the commuting congestion of train stations at the central business district (CBD). The conventional learning-and-optimization scheme needs accurate boarding/alighting demand to update the train fare in each trial. However, when congestion happens, the observed boarding/alighting demand will be larger than the actual boarding/alighting demand due to the delays and the longer dwelling time. Thus, the actual boarding/alighting demand is not available in practice. The improved algorithm deals with this issue by using inexact and less information to determine the new trial fare during the iteration. Namely, the improved method bypasses the conditions that may lead to biased results so as to significantly enhance the reliability of the learning-and-optimization method. The simplified algorithm also makes this method more practical. The convergence property of the proposed algorithm is rigorously proved and the convergence rate is demonstrated to be exponential. Numerical studies are performed to demonstrate the efficiency of the improved learning-and-optimization method.

ACS Style

Xinyuan Chen; Wei Zhang; Xiaomeng Guo; Zhiyuan Liu; Shuaian Wang. An improved learning-and-optimization train fare design method for addressing commuting congestion at CBD stations. Transportation Research Part E: Logistics and Transportation Review 2021, 153, 102427 .

AMA Style

Xinyuan Chen, Wei Zhang, Xiaomeng Guo, Zhiyuan Liu, Shuaian Wang. An improved learning-and-optimization train fare design method for addressing commuting congestion at CBD stations. Transportation Research Part E: Logistics and Transportation Review. 2021; 153 ():102427.

Chicago/Turabian Style

Xinyuan Chen; Wei Zhang; Xiaomeng Guo; Zhiyuan Liu; Shuaian Wang. 2021. "An improved learning-and-optimization train fare design method for addressing commuting congestion at CBD stations." Transportation Research Part E: Logistics and Transportation Review 153, no. : 102427.

Journal article
Published: 26 July 2021 in IEEE Transactions on Intelligent Transportation Systems
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Short-term estimation and prediction of pedestrian density in urban hot spots (e.g., railway station, shopping mall, etc.) is an important topic for traffic management and control in densely populated areas. In this paper, we propose a short-term pedestrian density estimation and prediction method based on mobile phone data. Firstly, pedestrian density in hot spots is estimated using mobile phone data. To decrease the positioning errors of mobile phone data, a modified particle filter method, which considers the movements of pedestrians, is applied for pre-processing the data. An efficient spatial access method (i.e., Hilbert R-tree) is adopted to construct pedestrians' position indexes for realizing the short-term estimation. Secondly, based on the estimation results, the spatiotemporal extended Kalman filter (SEKF) is proposed for the short-term prediction of pedestrian density. A massive mobile phone dataset collected in Nanjing, China is used in the case study. The estimated pedestrian density from Monday to Thursday is used for pedestrian density prediction on Friday. The results show that the proposed method can estimate and predict pedestrian density in hot spots, especially in small-scale sites of hot spots efficiently in a short time. Comparing with classical prediction methods, the proposed SEKF method predicts short-term pedestrian density in urban hot spots more accurately.

ACS Style

Jinbiao Huo; Xiao Fu; Zhiyuan Liu; Qi Zhang. Short-Term Estimation and Prediction of Pedestrian Density in Urban Hot Spots Based on Mobile Phone Data. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -12.

AMA Style

Jinbiao Huo, Xiao Fu, Zhiyuan Liu, Qi Zhang. Short-Term Estimation and Prediction of Pedestrian Density in Urban Hot Spots Based on Mobile Phone Data. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-12.

Chicago/Turabian Style

Jinbiao Huo; Xiao Fu; Zhiyuan Liu; Qi Zhang. 2021. "Short-Term Estimation and Prediction of Pedestrian Density in Urban Hot Spots Based on Mobile Phone Data." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-12.

Journal article
Published: 23 July 2021 in Transportmetrica B: Transport Dynamics
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The estimation of citywide passenger demand plays a vital role in system planning, operation, and management of the urban transit system. The Wi-Fi probe data, one of the emerging crowdsourcing data, is utilized to collect traces of smartphone users in this study. We establish a framework for OD matrix reconstruction, including extracting features for transit patronage and distinguishing them from non-transit users based on K-means clustering. Such a framework makes partial OD matrix more reliable. A probabilistic estimation method of bus OD matrix reconstruction is then proposed based on the partial OD matrix and the number of boarding and alighting passengers. A field study was carried out on bus line 5 in Suzhou, China. Compared to the measured ground truth, the difference in OD-level is 0.5–1.5 passengers per stop, showing that the proposed method for OD matrix reconstruction is reliable.

ACS Style

Yunshan Wang; Wenbo Zhang; Tianli Tang; Dazhong Wang; Zhiyuan Liu. Bus OD matrix reconstruction based on clustering Wi-Fi probe data. Transportmetrica B: Transport Dynamics 2021, 1 -16.

AMA Style

Yunshan Wang, Wenbo Zhang, Tianli Tang, Dazhong Wang, Zhiyuan Liu. Bus OD matrix reconstruction based on clustering Wi-Fi probe data. Transportmetrica B: Transport Dynamics. 2021; ():1-16.

Chicago/Turabian Style

Yunshan Wang; Wenbo Zhang; Tianli Tang; Dazhong Wang; Zhiyuan Liu. 2021. "Bus OD matrix reconstruction based on clustering Wi-Fi probe data." Transportmetrica B: Transport Dynamics , no. : 1-16.

Review
Published: 24 June 2021 in International Journal of Disaster Risk Reduction
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With the trend of global warming and destructive human activities, the frequent occurrences of catastrophes have posed devastating threats to human life and social stability worldwide. The emergency management (EM) system plays a significant role in saving people's lives and reducing property damage. The prediction system for the occurrence of emergency events and resulting impacts is widely recognized as the first stage of the EM system, the accuracy of which has a significant impact on the efficiency of resource allocation, dispatching, and evacuation. In fact, the number and variety of contributions to prediction techniques, such as statistic analysis, artificial intelligence, and simulation method, are exploded in recent years, motivating the need for a systematic analysis of the current works on disaster prediction. To this end, this paper presents a systematic review of contributions on prediction methods for emergency occurrence and resource demand of both natural and man-made disasters. Through a detailed discussion on the features of each type of emergency event, this paper presents a comprehensive survey of state-of-the-art prediction technologies which have been widely applied in EM. After that, we summarize the challenges of current efforts and point out future directions.

ACS Style

Di Huang; Shuaian Wang; Zhiyuan Liu. A systematic review of prediction methods for emergency management. International Journal of Disaster Risk Reduction 2021, 62, 102412 .

AMA Style

Di Huang, Shuaian Wang, Zhiyuan Liu. A systematic review of prediction methods for emergency management. International Journal of Disaster Risk Reduction. 2021; 62 ():102412.

Chicago/Turabian Style

Di Huang; Shuaian Wang; Zhiyuan Liu. 2021. "A systematic review of prediction methods for emergency management." International Journal of Disaster Risk Reduction 62, no. : 102412.

Journal article
Published: 23 June 2021 in IEEE Intelligent Transportation Systems Magazine
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ACS Style

Zhichen Liu; Zhiyuan Liu; Xiao Fu. Dynamic Origin-Destination Flow Prediction Using Spatial-Temporal Graph Convolution Network With Mobile Phone Data. IEEE Intelligent Transportation Systems Magazine 2021, PP, 2 -15.

AMA Style

Zhichen Liu, Zhiyuan Liu, Xiao Fu. Dynamic Origin-Destination Flow Prediction Using Spatial-Temporal Graph Convolution Network With Mobile Phone Data. IEEE Intelligent Transportation Systems Magazine. 2021; PP (99):2-15.

Chicago/Turabian Style

Zhichen Liu; Zhiyuan Liu; Xiao Fu. 2021. "Dynamic Origin-Destination Flow Prediction Using Spatial-Temporal Graph Convolution Network With Mobile Phone Data." IEEE Intelligent Transportation Systems Magazine PP, no. 99: 2-15.

Research article
Published: 11 June 2021 in Transportmetrica A: Transport Science
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An improved parallel block coordinate descent (iPBCD) algorithm for solving the user equilibrium traffic assignment problem is proposed. The iPBCD algorithm is developed based on the parallel block coordinate descent algorithm (PBCD). The hybrid flow update policy is investigated to enhance the robustness and performance of the PBCD algorithm. Two update order rules, namely a cyclic rule and a greedy rule are compared for block indices. Then, the block size is optimized using a sensitivity analysis test. Finally, five index-grouping rules are tested for comparative purposes. Numerical experiments indicate that index-grouping rules have a significant influence on convergence: the information-based drop-out rule performs better in terms of convergence and efficiency.

ACS Style

Zewen Wang; Kai Zhang; Xinyuan Chen; Meng Wang; Renwei Liu; Zhiyuan Liu. An improved parallel block coordinate descent method for the distributed computing of traffic assignment problem. Transportmetrica A: Transport Science 2021, 1 -32.

AMA Style

Zewen Wang, Kai Zhang, Xinyuan Chen, Meng Wang, Renwei Liu, Zhiyuan Liu. An improved parallel block coordinate descent method for the distributed computing of traffic assignment problem. Transportmetrica A: Transport Science. 2021; ():1-32.

Chicago/Turabian Style

Zewen Wang; Kai Zhang; Xinyuan Chen; Meng Wang; Renwei Liu; Zhiyuan Liu. 2021. "An improved parallel block coordinate descent method for the distributed computing of traffic assignment problem." Transportmetrica A: Transport Science , no. : 1-32.

Journal article
Published: 25 May 2021 in IEEE Transactions on Intelligent Transportation Systems
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We investigate how to effectively and efficiently embed users' personalized travel behaviors to vectors in this paper. Based on an example scenario of travel mode choice in intelligent transportation system, three data structures representing users' travel behaviors are defined, namely heterogeneous graph of users' travel behaviors, user travel behavior k-partite graph, and personalized user travel behavior sentence set. This paper systematically analyzes the principle of existing methods and provides intuitions for the problem of learning travel behavior representation in intelligent transportation system. Then we propose the Behavior2vector, which is an improved method tailored for embedding users' personalized travel behaviors to vectors. In our experiments, we design a travel mode choice model based on machine learning, which uses both hand-crafted basic features and embedded vector features. We further quantify the impact of various factors on travel mode choice and use travel big data to test the hypothesis of traffic assignment models, e.g., travelers always choose the path with the shortest path. In addition, we also compared with the existing graph embedding methods and essentially discussed their advantages and disadvantages.

ACS Style

Yang Liu; Fanyou Wu; Cheng Lyu; Xin Liu; Zhiyuan Liu. Behavior2vector: Embedding Users' Personalized Travel Behavior to Vector. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -10.

AMA Style

Yang Liu, Fanyou Wu, Cheng Lyu, Xin Liu, Zhiyuan Liu. Behavior2vector: Embedding Users' Personalized Travel Behavior to Vector. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-10.

Chicago/Turabian Style

Yang Liu; Fanyou Wu; Cheng Lyu; Xin Liu; Zhiyuan Liu. 2021. "Behavior2vector: Embedding Users' Personalized Travel Behavior to Vector." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-10.

Journal article
Published: 13 May 2021 in IEEE Transactions on Intelligent Transportation Systems
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The integrity of bus route information is fundamental to the analysis of operating status and travel pattern of urban public transport system. However, due to the malfunction of bus positioning devices or delayed update of database, the route information stored in positioning devices might be lost or erroneous. To address this issue, this paper designs a framework matching the bus trajectory with a set of predefined bus routes. The trajectories are first partitioned into segments using the spatio-temporal DBSCAN. Then, the curve similarity between trajectories and bus routes is calculated based on the metric of partial Fréchet distance, which searches for a best mapping between curves that minimizes the maximum distance between vertex pairs. A directed-acyclic-graph-based method is also proposed to compute the partial Fréchet distance. Finally, the best match for each trajectory is given based on the relative ranking. The proposed framework is evaluated on the bus trajectory data of Fuyang and Shenzhen in China. The experimental results demonstrate that the framework can well identify the underlying routes according to the recorded bus trajectories and suggest which route information needs updating.

ACS Style

Cheng Lyu; Xinhua Wu; Yang Liu; Zhiyuan Liu. A Partial-Fréchet-Distance-Based Framework for Bus Route Identification. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -6.

AMA Style

Cheng Lyu, Xinhua Wu, Yang Liu, Zhiyuan Liu. A Partial-Fréchet-Distance-Based Framework for Bus Route Identification. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-6.

Chicago/Turabian Style

Cheng Lyu; Xinhua Wu; Yang Liu; Zhiyuan Liu. 2021. "A Partial-Fréchet-Distance-Based Framework for Bus Route Identification." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-6.

Journal article
Published: 28 April 2021 in Sustainability
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This paper investigates the optimal congestion pricing problem that considers day-to-day evolutionary flow dynamics. Under the circumstance that traffic flows evolve from day to day and the system might be in a non-equilibrium state during a certain period of days after implementing (or adjusting) a congestion toll scheme, it is questionable to use an equilibrium-based index under steady state as the objective to measure the performance of a congestion toll scheme. To this end, this paper proposes a mean–variance-based congestion pricing scheme, which is a robust optimization model, to consider the evolution process of traffic flow dynamics in the optimal toll design problem. More specifically, in the mean–variance-based toll scheme, travelers aim to minimize the variance of expected total travel costs (ETTCs) on different days to reduce risk in daily travels, while the average ETTC over the whole planning period is restricted to being no larger than a predetermined target value set by the authorities. A metaheuristic approach based on the whale optimization algorithm is designed to solve the proposed mean–variance-based day-to-day dynamic congestion pricing problem. Finally, a numerical experiment is conducted to validate the effectiveness of the proposed model and solution algorithm. Results show that the used 9-node network can reach a steady state within 18 days after implementing the mean–variance-based congestion pricing, and the optimal toll scheme can be also obtained with this toll strategy.

ACS Style

Qixiu Cheng; Jun Chen; Honggang Zhang; Zhiyuan Liu. Optimal Congestion Pricing with Day-to-Day Evolutionary Flow Dynamics: A Mean–Variance Optimization Approach. Sustainability 2021, 13, 4931 .

AMA Style

Qixiu Cheng, Jun Chen, Honggang Zhang, Zhiyuan Liu. Optimal Congestion Pricing with Day-to-Day Evolutionary Flow Dynamics: A Mean–Variance Optimization Approach. Sustainability. 2021; 13 (9):4931.

Chicago/Turabian Style

Qixiu Cheng; Jun Chen; Honggang Zhang; Zhiyuan Liu. 2021. "Optimal Congestion Pricing with Day-to-Day Evolutionary Flow Dynamics: A Mean–Variance Optimization Approach." Sustainability 13, no. 9: 4931.

Journal article
Published: 22 March 2021 in Transportation Research Part C: Emerging Technologies
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The emergence of navigation applications with multi-modal trip planning services has brought about the demand for the multi-modal transportation recommendation systems. In this paper, we explore the problem of large-scale multi-modal transportation recommendation and propose a novel travel mode recommendation system for a multi-modal transportation system. In the proposed model, the feature engineering focuses on the application scenario of the multi-modal transportation recommendation, and is designed from multiple perspectives of users, travel modes, locations, and time. To learn a better representation of the co-occurrence, we construct a bipartite graph for the Origin-Destination (OD) pair and the User-OD pair of all the query records then transformed nodes in the bipartite graph to feature vectors using a graph-embedding technique. Finally, we propose a post-processing technique to handle the inconsistency between the objective function and evaluation metric. Experimental results from a city-wide multi-modal transportation recommendation indicate that our proposed model is superior to the existing method of navigation service providers.

ACS Style

Yang Liu; Cheng Lyu; Zhiyuan Liu; Jinde Cao. Exploring a large-scale multi-modal transportation recommendation system. Transportation Research Part C: Emerging Technologies 2021, 126, 103070 .

AMA Style

Yang Liu, Cheng Lyu, Zhiyuan Liu, Jinde Cao. Exploring a large-scale multi-modal transportation recommendation system. Transportation Research Part C: Emerging Technologies. 2021; 126 ():103070.

Chicago/Turabian Style

Yang Liu; Cheng Lyu; Zhiyuan Liu; Jinde Cao. 2021. "Exploring a large-scale multi-modal transportation recommendation system." Transportation Research Part C: Emerging Technologies 126, no. : 103070.

Research article
Published: 10 December 2020 in Transportmetrica A: Transport Science
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The bus stop-skipping scheme is designed to reduce the number of stops and increase bus operating speed. But in a mixed traffic environment, buses can hardly maintain a high and stable operating speed due to frequent merging and overtaking activities. In this regard, the exclusive bus lane is used to separate buses from normal traffic. However, the permanent bus lane would inevitably decrease road capacity and result in more congestions along the bus lane, especially during peak hours. This paper proposes a hybrid bus operational scheme combining the bus lane reservation strategy and the stop-skipping control. Considering the uncertainty of passenger demand, this paper proposes a multi-stage stochastic programming model. The evolution of the uncertainty is interpreted by a scenario tree. A progressive hedging-based method is developed to separate the large-scale optimization problem into sub-problems. Two numerical experiments are conducted to verify the proposed model and solution algorithm.

ACS Style

Di Huang; Jiping Xing; Zhiyuan Liu; Qinhe An. A multi-stage stochastic optimization approach to the stop-skipping and bus lane reservation schemes. Transportmetrica A: Transport Science 2020, 17, 1272 -1304.

AMA Style

Di Huang, Jiping Xing, Zhiyuan Liu, Qinhe An. A multi-stage stochastic optimization approach to the stop-skipping and bus lane reservation schemes. Transportmetrica A: Transport Science. 2020; 17 (4):1272-1304.

Chicago/Turabian Style

Di Huang; Jiping Xing; Zhiyuan Liu; Qinhe An. 2020. "A multi-stage stochastic optimization approach to the stop-skipping and bus lane reservation schemes." Transportmetrica A: Transport Science 17, no. 4: 1272-1304.

Journal article
Published: 22 October 2020 in Transportation Research Part E: Logistics and Transportation Review
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This paper proposes an adding-runs strategy to alleviate in-vehicle crowding for peak-hour bus services. Passengers’ departure time choices under user equilibrium and system optimum conditions are investigated with and without adding-runs strategy. A bi-level programming model is developed to determine the optimal adding-runs strategy. An artificial bee colony algorithm is adopted to solve the proposed bi-level problem. Numerical examples show that the adding-runs strategy is effective in alleviating crowding effects and reducing schedule delay in peak-hour bus services. The total system cost can be reduced by more than 8% with the optimal adding-runs strategy.

ACS Style

Qinhe An; Xiao Fu; Di Huang; Qixiu Cheng; Zhiyuan Liu. Analysis of adding-runs strategy for peak-hour regular bus services. Transportation Research Part E: Logistics and Transportation Review 2020, 143, 102100 .

AMA Style

Qinhe An, Xiao Fu, Di Huang, Qixiu Cheng, Zhiyuan Liu. Analysis of adding-runs strategy for peak-hour regular bus services. Transportation Research Part E: Logistics and Transportation Review. 2020; 143 ():102100.

Chicago/Turabian Style

Qinhe An; Xiao Fu; Di Huang; Qixiu Cheng; Zhiyuan Liu. 2020. "Analysis of adding-runs strategy for peak-hour regular bus services." Transportation Research Part E: Logistics and Transportation Review 143, no. : 102100.

Journal article
Published: 07 October 2020 in Transportation Research Part C: Emerging Technologies
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This paper presents a Parallel Block-Coordinate Descent (PBCD) algorithm for solving the user equilibrium traffic assignment problem. Most of the existing algorithms for the user equilibrium-based traffic assignment problem are developed and implemented sequentially. This paper aims to study and investigate the parallel computing approach to utilize the widely available parallel computing resources. The PBCD algorithm is developed based on the state-of-the-art path-based algorithm, i.e., the improved path-based gradient projection algorithm (iGP). The computationally expensive components in the iGP are identified and parallelized. A parallel block-coordinate method is proposed to replace the widely used Gauss-Seidel method for the procedure of path flow adjustment. A new rule is proposed to group OD pairs into different blocks. The numerical examples show that the implemented PBCD algorithm can significantly reduce the computing time.

ACS Style

Xinyuan Chen; Zhiyuan Liu; Kai Zhang; Zewen Wang. A parallel computing approach to solve traffic assignment using path-based gradient projection algorithm. Transportation Research Part C: Emerging Technologies 2020, 120, 102809 .

AMA Style

Xinyuan Chen, Zhiyuan Liu, Kai Zhang, Zewen Wang. A parallel computing approach to solve traffic assignment using path-based gradient projection algorithm. Transportation Research Part C: Emerging Technologies. 2020; 120 ():102809.

Chicago/Turabian Style

Xinyuan Chen; Zhiyuan Liu; Kai Zhang; Zewen Wang. 2020. "A parallel computing approach to solve traffic assignment using path-based gradient projection algorithm." Transportation Research Part C: Emerging Technologies 120, no. : 102809.

Journal article
Published: 24 September 2020 in IEEE Transactions on Intelligent Transportation Systems
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This paper investigates the spatiotemporal characteristics and predictability of the emerging modern traffic behavior, ridesourcing. We collect a comprehensive data set of Didi ridesourcing cars on a large geographical scale of a capital city in China, including both the temporal order information and the GPS-recorded spatial trajectories. To extract the features of this kind of traffic behavior, we construct a large-scale network by considering every traffic flow of the orders. Therein, a driver consecutively visiting different regions of the city connects the relationship of these sites. The weighted ridesourcing network shows a consistency of the distribution of trip orders and the Clark model for population distribution. The network also has spatial and temporal features with power laws, sometimes with exponential truncations and log-normal distributions. Furthermore, we propose a general analytical method to quantify the predictability of this kind of behavior by calculating the entropy at a collective level, which can be extended to quantify other traffic behaviors. Finally, by considering the traffic congestion factor, we propose a better neural network based model for predicting dwelling time of the ridesourcing behavior. We suggest that the traffic behavior of ridesourcing cars indicates specific non-Markovian characteristics, which can be systematically analyzed from the viewpoint of network sciences.

ACS Style

Duxin Chen; Qi Shao; Zhiyuan Liu; Wenwu Yu; C. L. Philip Chen. Ridesourcing Behavior Analysis and Prediction: A Network Perspective. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -10.

AMA Style

Duxin Chen, Qi Shao, Zhiyuan Liu, Wenwu Yu, C. L. Philip Chen. Ridesourcing Behavior Analysis and Prediction: A Network Perspective. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-10.

Chicago/Turabian Style

Duxin Chen; Qi Shao; Zhiyuan Liu; Wenwu Yu; C. L. Philip Chen. 2020. "Ridesourcing Behavior Analysis and Prediction: A Network Perspective." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-10.

Articles
Published: 18 August 2020 in Transportmetrica A: Transport Science
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Over the past decades, the impact of transport operating strategy improvements on space-time accessibility, which is an important research area for network design problem, has not been explicitly investigated particularly with the use of activity-based approach. In this paper, a novel activity-based space-time accessibility measure is introduced for considering individuals’ accessibility to various activities and travels in a unified super-network framework. A bi-level programming model is proposed for optimizing time-dependent transit line headways and fares in a multi-modal transit network from the activity-based space-time accessibility perspective. In the upper level, transit line headways and fares are optimized by time of day to maximize the network-wide activity-based space-time accessibility. At the lower level, an activity-based network equilibrium model is adapted to provide the resultant activity-travel patterns as reactions to the upper level decision. A simplified network in Hong Kong selected area is used to illustrate the application of the proposed model.

ACS Style

Xiao Fu; William H.K. Lam; Bi Yu Chen; Zhiyuan Liu. Maximizing space-time accessibility in multi-modal transit networks: an activity-based approach. Transportmetrica A: Transport Science 2020, 1 -29.

AMA Style

Xiao Fu, William H.K. Lam, Bi Yu Chen, Zhiyuan Liu. Maximizing space-time accessibility in multi-modal transit networks: an activity-based approach. Transportmetrica A: Transport Science. 2020; ():1-29.

Chicago/Turabian Style

Xiao Fu; William H.K. Lam; Bi Yu Chen; Zhiyuan Liu. 2020. "Maximizing space-time accessibility in multi-modal transit networks: an activity-based approach." Transportmetrica A: Transport Science , no. : 1-29.

Articles
Published: 10 August 2020 in Transportmetrica A: Transport Science
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This paper develops a real-time agent-based simulation model to optimize the dynamic bus stop-skipping and holding schemes. The proposed agent-based simulation model is capable of modelling, scheduling and solving control strategy problems in real-time complex, dynamic and stochastic scenarios of the transport systems. To this end, random bus travel times and passenger arrivals are incorporated in the agent-based model to evaluate the integrated bus operation strategies. Apart from the total costs, the performance of the proposed strategies is evaluated with respect to the buses’ headway deviation and reliability index, the passengers’ travel time and the overall system cost respectively. The results show the benefits of different strategies under different scenarios. The RTSSH (real-time stop-skipping and holding) strategy performs the best when buses are not reused, whilst RTSS (real-time stop-skipping) outperforms RTSSH strategy when buses are allowed to be reused.

ACS Style

Lele Zhang; Jiangyan Huang; Zhiyuan Liu; Hai L. Vu. An agent-based model for real-time bus stop-skipping and holding schemes. Transportmetrica A: Transport Science 2020, 17, 615 -647.

AMA Style

Lele Zhang, Jiangyan Huang, Zhiyuan Liu, Hai L. Vu. An agent-based model for real-time bus stop-skipping and holding schemes. Transportmetrica A: Transport Science. 2020; 17 (4):615-647.

Chicago/Turabian Style

Lele Zhang; Jiangyan Huang; Zhiyuan Liu; Hai L. Vu. 2020. "An agent-based model for real-time bus stop-skipping and holding schemes." Transportmetrica A: Transport Science 17, no. 4: 615-647.

Journal article
Published: 04 August 2020 in Transportation Research Part E: Logistics and Transportation Review
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This paper addresses a static bike repositioning problem by embedding a short-term demand forecasting process, the Random Forest (RF) model, to account for the demand dynamics in the daytime. To tackle the heterogeneous repositioning fleets, a novel repositioning operation strategy constructed on the hub-and-spoke network framework is proposed. The repositioning optimization model is formulated using mixed-integer programming. An artificial bee colony algorithm, integrated with a commercial solver, is applied to address computational complexity. Experimental results show that the RF can achieve a high forecasting accuracy, and the proposed repositioning strategy can efficiently decrease the users’ dissatisfaction.

ACS Style

Di Huang; Xinyuan Chen; Zhiyuan Liu; Cheng Lyu; Shuaian Wang; Xuewu Chen. A static bike repositioning model in a hub-and-spoke network framework. Transportation Research Part E: Logistics and Transportation Review 2020, 141, 102031 .

AMA Style

Di Huang, Xinyuan Chen, Zhiyuan Liu, Cheng Lyu, Shuaian Wang, Xuewu Chen. A static bike repositioning model in a hub-and-spoke network framework. Transportation Research Part E: Logistics and Transportation Review. 2020; 141 ():102031.

Chicago/Turabian Style

Di Huang; Xinyuan Chen; Zhiyuan Liu; Cheng Lyu; Shuaian Wang; Xuewu Chen. 2020. "A static bike repositioning model in a hub-and-spoke network framework." Transportation Research Part E: Logistics and Transportation Review 141, no. : 102031.

Journal article
Published: 15 July 2020 in IEEE Transactions on Intelligent Transportation Systems
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Deep Neural Network (DNN) has been applied in a wide range of fields due to its exceptional predictive power. In this paper, we explore how to use DNN to solve the large-scale bus passenger flow prediction problem. Currently, most existing methods designed for the passenger flow prediction problem are based on a single view, which is insufficient to capture the dynamics in passenger flow fluctuation. Thus, we analyze the passenger flow from scopes on both macroscopic and microscopic levels, in order to take full advantage of the information from a variety of views. To better understand the role of different views, decision-tree-based models are used in modeling and predicting passenger flow. The defects and key features of decision-tree-based models are then analyzed. The results of the analysis can assist the architecture design of the deep learning network. Inspired by the feature engineering of decision-tree-based models, a modular convolutional neural network is designed, which contains automatic feature extraction block, feature importance block, fully-connected block, and data fusion block. The proposed model is evaluated on the city-wide public transport datasets in Nanjing, China, involving 1,091 bus lines in total. The experiment results demonstrate the outstanding performance of the proposed method in real situations.

ACS Style

Yang Liu; Cheng Lyu; Xin Liu; Zhiyuan Liu. Automatic Feature Engineering for Bus Passenger Flow Prediction Based on Modular Convolutional Neural Network. IEEE Transactions on Intelligent Transportation Systems 2020, 1 -10.

AMA Style

Yang Liu, Cheng Lyu, Xin Liu, Zhiyuan Liu. Automatic Feature Engineering for Bus Passenger Flow Prediction Based on Modular Convolutional Neural Network. IEEE Transactions on Intelligent Transportation Systems. 2020; (99):1-10.

Chicago/Turabian Style

Yang Liu; Cheng Lyu; Xin Liu; Zhiyuan Liu. 2020. "Automatic Feature Engineering for Bus Passenger Flow Prediction Based on Modular Convolutional Neural Network." IEEE Transactions on Intelligent Transportation Systems , no. 99: 1-10.

Journal article
Published: 06 July 2020 in IEEE Transactions on Cybernetics
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The accurate prediction of online taxi-hailing demand is challenging but of significant value in the development of the intelligent transportation system. This article focuses on large-scale online taxi-hailing demand prediction and proposes a personalized demand prediction model. A model with two attention blocks is proposed to capture both spatial and temporal perspectives. We also explored the impact of network architecture on taxi-hailing demand prediction accuracy. The proposed method is universal in the sense that it is applicable to problems associated with large-scale spatiotemporal prediction. The experimental results on city-wide online taxi-hailing demand dataset demonstrate that the proposed personalized demand prediction model achieves superior prediction accuracy.

ACS Style

Zhiyuan Liu; Yang Liu; Cheng Lyu; Jieping Ye. Building Personalized Transportation Model for Online Taxi-Hailing Demand Prediction. IEEE Transactions on Cybernetics 2020, 1 -9.

AMA Style

Zhiyuan Liu, Yang Liu, Cheng Lyu, Jieping Ye. Building Personalized Transportation Model for Online Taxi-Hailing Demand Prediction. IEEE Transactions on Cybernetics. 2020; (99):1-9.

Chicago/Turabian Style

Zhiyuan Liu; Yang Liu; Cheng Lyu; Jieping Ye. 2020. "Building Personalized Transportation Model for Online Taxi-Hailing Demand Prediction." IEEE Transactions on Cybernetics , no. 99: 1-9.

Articles
Published: 03 July 2020 in International Journal of Sustainable Transportation
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In recent years, many cities around the world have implemented bike-sharing programs. A number of studies on the relationship between the built environment and bike usage have provided important insights into understanding bike-sharing systems. However, the effects of the built environment on the structural properties of bike-sharing networks are seldom discussed in the literature. This research proposes a novel and interdisciplinary framework to explore how built environment factors affect the topological properties of bike-sharing networks. Firstly, this research applies a complex network approach to quantify the importance of bike stations in the network. Then, multisource data are utilized to identify comprehensive built environment attributes. Finally, spatial regression models are used to reveal the relationship between the importance of bike stations and built environment. In this study, the bike-sharing system in Suzhou, China, is taken as a case study. The empirical result shows that the importance of bike stations displays strong spatial dependence. Also, built environment attributes such as resident population, accessibility to subway stations, the capacity of bike stations, and the total length of main roads within a catchment area have different effects on the importance of bike stations. It should be noted that the floating population and the number of bus stops surrounding bike stations do not have strong correlations with the importance of bike stations. The findings of this study can guide urban planners and operators to improve the service quality and resilience of bike-sharing systems.

ACS Style

Chunliang Wu; HyungChul Chung; Zhiyuan Liu; Inhi Kim. Examining the effects of the built environment on topological properties of the bike-sharing network in Suzhou, China. International Journal of Sustainable Transportation 2020, 15, 338 -350.

AMA Style

Chunliang Wu, HyungChul Chung, Zhiyuan Liu, Inhi Kim. Examining the effects of the built environment on topological properties of the bike-sharing network in Suzhou, China. International Journal of Sustainable Transportation. 2020; 15 (5):338-350.

Chicago/Turabian Style

Chunliang Wu; HyungChul Chung; Zhiyuan Liu; Inhi Kim. 2020. "Examining the effects of the built environment on topological properties of the bike-sharing network in Suzhou, China." International Journal of Sustainable Transportation 15, no. 5: 338-350.