Towards Better Bus Networks: a Visual Analytics Approach
Total Page：16
File Type：pdf, Size：1020Kb
© 2020 IEEE. This is the author’s version of the article that has been published in IEEE Transactions on Visualization and Computer Graphics. The final version of this record is available at: 10.1109/TVCG.2020.3030458 Towards Better Bus Networks: A Visual Analytics Approach Di Weng, Chengbo Zheng, Zikun Deng, Mingze Ma, Jie Bao, Yu Zheng, Mingliang Xu, Yingcai Wu A B C D 6 P.M. AL DR PV 9 A.M. SC NS E RL F G Route #696 H West East Fig. 1. The interface of BNVA. A, C, G, H) The pattern indicated by the transfers from Route #683 to #696 suggests that Route #683 can be extended farther into the west. B) The network-level analysis, comprising the map, route, and aggregation layers, facilitates the exploration of network performance. D) The zone glyph summarizes the statistics and passenger flows of a transportation zone. E) The toolbox provides fine-grained controls for the model. F) The route ranking view depicts the performance criteria of the routes. Abstract—Bus routes are typically updated every 3–5 years to meet constantly changing travel demands. However, identifying deficient bus routes and finding their optimal replacements remain challenging due to the difficulties in analyzing a complex bus network and the large solution space comprising alternative routes. Most of the automated approaches cannot produce satisfactory results in real-world settings without laborious inspection and evaluation of the candidates. The limitations observed in these approaches motivate us to collaborate with domain experts and propose a visual analytics solution for the performance analysis and incremental planning of bus routes based on an existing bus network. Developing such a solution involves three major challenges, namely, a) the in-depth analysis of complex bus route networks, b) the interactive generation of improved route candidates, and c) the effective evaluation of alternative bus routes. For challenge a, we employ an overview-to-detail approach by dividing the analysis of a complex bus network into three levels to facilitate the efficient identification of deficient routes. For challenge b, we improve a route generation model and interpret the performance of the generation with tailored visualizations. For challenge c, we incorporate a conflict resolution strategy in the progressive decision-making process to assist users in evaluating the alternative routes and finding the most optimal one. The proposed system is evaluated with two usage scenarios based on real-world data and received positive feedback from the experts. arXiv:2008.10915v3 [cs.HC] 5 Nov 2020 Index Terms—Bus route planning, spatial decision-making, urban data visual analytics. 1 INTRODUCTION In the last few centuries, the public transit bus service has become one of the most widely deployed public transportation backbones worldwide • D. Weng, Z. Deng, and Y. Wu are with State Key Lab of CAD&CG, Zhejiang because of its flexibility and affordability. Bus routes are typically University, Hangzhou, China and Zhejiang Lab, Hangzhou, China. E-mail: updated every 3–5 years because the travel demand of bus riders is fdweng, zikun rain, [email protected]. Y. Wu is the corresponding author. constantly changing [44]. However, revising bus routes remains difficult • C. Zheng and M. Ma are with Zhejiang Lab, Hangzhou, China. Email: because a) it is hard to analyze the performance of a huge and complex [email protected], [email protected]. bus network to determine which routes are problematic, and b) a large • Jie Bao and Yu Zheng are with JD Intelligent Cities Research, Beijing, solution space constituted by many factors involved in the planning China and JD Intelligent Cities Business Unit, JD Digits, Beijing, China. E-mail: [email protected], [email protected]. Manuscript received xx xxx. 201x; accepted xx xxx. 201x. Date of Publication • Mingliang Xu is with School of Information Engineering, Zhengzhou xx xxx. 201x; date of current version xx xxx. 201x. For information on University, Zhengzhou, China and Henan Institute of Advanced Technology, obtaining reprints of this article, please send e-mail to: [email protected]. Zhengzhou University, Zhengzhou, China. E-mail: Digital Object Identifier: xx.xxxx/TVCG.201x.xxxxxxx [email protected]. 1 process [61] needs to be extensively evaluated. 2 RELATED WORK Most bus networks are planned and updated manually or by ana- This section summarizes the transit network design and urban data lyzing small datasets based on planners’ knowledge [22, 47, ]. Such visual analytics studies that are closely related to the present study. methods can be laborious and time-consuming. To identify feasible bus routes, data-driven planning methods [29, 46, 55], including the 2.1 Transit Network Design mathematical and heuristic ones, have been proposed to extract the Transit network design is a huge and complex research topic in trans- routes based on predefined criteria. However, most of these methods portation research. Guihaire et al. [29] divided the process of transit act as a black box, generating an optimized result based on the given network design and scheduling into three parts: transit network design input data and parameters without explanations. Therefore, verifying problem (TNDP), transit network frequencies setting problem, and the quality of the generated routes or adjusting the parameters to find an transit network timetabling problem. This section mainly focuses on improved solution is difficult for domain experts. The interpretability the related studies on the TNDP. of route generation has been further studied by Chen et al. [17] and Many data-driven methods have been developed to generate transit Weng et al. [63]. Rather than producing a potentially unsatisfactory networks automatically based on public travel demand, which can be route, they propose to generate a set of Pareto-optimal transit routes, determined from the survey [60, 70], transportation [17], and telecom- where none of the routes is better than any other route for all given munication [56] data. These methods can be categorized into math- criteria. However, experts are still required to manually compare among ematical and heuristic methods. Mathematical methods [28, 45, 51] hundreds of routes and determine the most optimal one. generate the transit line configuration with numeric optimization ap- In this study, we collaborated with the experts from the transportation proaches, including mixed-integer linear programming [32]. Heuristic and urban computing domains to develop a solution that facilitates methods tackle the problem based on certain heuristics with the greedy efficient analysis and incremental planning of bus routes. Inspired by approach [46], heuristic search, and genetic algorithms (GAs), such as the urban data analytics studies [39, 64], we employ a visual analytics tabu search [77], GA variants [16,68], simulated annealing [21], and ant approach to integrate domain knowledge with the computational power colony algorithm [70]. However, most of these methods only generate of the Pareto-optimal route extraction method [63], thereby enabling an optimized transit line configuration balancing multiple performance the experts to interactively interpret the performance of complex bus criteria with the given parameters. To expand the decision-making networks and evaluate the alternative routes generated in real-time. scope with alternative solutions, Chen et al. [17] applied a random Developing such a solution poses the following three challenges: search approach in finding a set of transit routes that do not dominate In-depth analysis of complex bus route networks. The performance each other in terms of two criteria, namely, route time and passenger of bus route networks must be extensively evaluated first to detect the flow, with the given origin and destination stops. Weng et al. [63] elabo- ineffective parts that need to be replanned. However, bus route networks rated on the definition of Pareto-optimal transit routes and proposed an that operate in a large city may comprise a hierarchy of thousands of efficient route extraction method based on the Monte-Carlo tree search. bus routes and stations, producing millions of trip records per month. Nevertheless, the aforementioned automated methods may generate Analyzing such a complex dataset for deficient routes poses challenges unsatisfactory results and typically require users to laboriously analyze in the scalability and effectiveness of the proposed solution. the results and adjust the input parameters accordingly. Several prelim- Interactive generation of improved route candidates. The Pareto- inary efforts [38, 53] in providing interactive solutions for the design optimal route search model [63] can be used to generate potential route procedure have been observed in the wild, but these solutions do not alternatives to the detected deficient routes. However, the constraints involve model-assisted visual planning of transit routes. To the best of provided by the model are insufficient for many practical scenarios, for our knowledge, this study is the first step toward the in-depth visual example, planning the routes via a few key stops. Moreover, since the analytics of interactive bus route analysis and adjustment. model is progressive and time-consuming, the experts need to know 2.2 Visual Analytics of Urban Data when the results are sufficiently good to stop the search. Effective evaluation of alternative bus routes. A large number of Large-scale urban data, such as human mobility, social network, geo- route alternatives