Congestion Avoidance Routing Based on Large-Scale Social Signals Kun He, Zhongzhi Xu, Pu Wang, Lianbo Deng, and Lai Tu
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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 17, NO. 9, SEPTEMBER 2016 2613 Congestion Avoidance Routing Based on Large-Scale Social Signals Kun He, Zhongzhi Xu, Pu Wang, Lianbo Deng, and Lai Tu Abstract—The emergence of large-scale social signal data has Fundamentally, congestion is caused by an imbalance be- provided unprecedented opportunities to develop techniques for tween network capacity and travel demand, and it can be improving transportation systems. In this paper, we use two types alleviated by enhancing a network’s capacity [6], [7], reducing of social signal data, namely, mobile phone data and subway card data, to investigate congestion avoidance routing methodologies the volume of travel demand [1], [8]–[10], and by intelligently in the Beijing subway and San Francisco road networks. The inducing travelers to use proper routes [11], [12]. Studies have social signal data were used to estimate detailed travel demand shown that increasing the capacity of important backbones information and to target sources of congestion, in order to or removing specific segments of transportation networks can develop intelligent routing models. We study two fundamental improve their transport efficiency [6], [11]. It has also been routing scenarios, namely, the shortest path (SP) scenario and the minimum cost (MC) scenario, and propose a hybrid routing model found that traffic congestion in road networks can be effec- that combines SP routing and MC routing. The hybrid model tively mitigated by intelligently reducing a small fraction of requires only a small fraction of travelers to take MC routes, travel demand [1]. These studies, which focus on improving but achieves nearly the same effect as MC routing. To apply the transportation network or reducing travel demand, offer support proposed routing methodologies in practical situations, we develop for long-term transportation planning. However, they are often an information-releasing framework to suggest routes for a small group of travelers whose route adjustments can significantly im- impractical in real-time traffic control, because it is not easy to prove the efficiency of the transportation networks. manipulate people’s travel plans or to increase the capacity of transportation networks. On the other hand, methods based on Index Terms—Social signal data, intelligent transportation sys- tems, routing models. intelligent routing avoid the need for change in people’s travel demand or in a transportation network’s structure and thus are easier to put into practice. I. INTRODUCTION With the fast development of electronic and sensing tech- HE fast growing demand for transportation in urban areas niques, individual portable devices such as smart phones have T has put immense pressure on transportation networks and experienced rapid growth and generated huge volumes of social has generated severe congestion not only on roads, but also signals [13]. Social signals, from GPS trajectories to mobile on buses and subways [1]–[4]. Congestion on roads causes a phone billing records and messages on the social networking, tremendous waste of time and energy, and congestion on public record our daily spatio-temporal information and create large transportation significantly reduces the riding comfortability of amounts of data for traffic and transportation analysis. In this travelers and may also trigger safety and health related concerns paper, we employ large-scale social signal data to estimate [3]. Mitigating congestion in urban transportation systems is a detailed travel demand, to target travelers who experience se- significant part of building smart, safe, and sustainable cities vere congestion, and to develop intelligent routing models for [5]. Many researchers from both scientific and engineering helping targeted travelers avoid congestion. We test the perfor- fields are dedicated to this research topic [6]–[12]. mance and validate the feasibility of the models on the Beijing subway and the San Francisco road networks. Our analysis shows that the proposed models can better route travelers and use transportation networks more appropriately. Manuscript received April 26, 2015; revised July 8, 2015 and August 12, The paper is organized as follows. Section II summarizes 2015; accepted August 18, 2015. Date of publication November 26, 2015; date related works on routing models. Section III introduces meth- of current version August 25, 2016. This work was supported in part by the National Natural Science Foundation of China under Grants 61473320 and ods for generating the transportation networks and the frame- 51208520, by the Fok Ying Tong Education Foundation under Grant 141075, works for estimating travel demand based on social signal data. and by the Project of Innovation-driven Plan in Central South University. The Section IV discusses two fundamental routing algorithms: Associate Editor for this paper was W. Chen. (Corresponding author: Pu Wang.) K. He, Z. Xu, P. Wang, and L. Deng are with the School of Traffic and Trans- shortest path (SP) routing and minimum cost (MC) routing. portation Engineering, Central South University, Changsha 410000, China Section V compares the routes for the two fundamental routing (e-mail: [email protected]; [email protected]; [email protected]. scenarios. Section VI introduces a hybrid routing model that cn; [email protected]). L. Tu is with the School of Electronic Information and Communications, combines SP routing and MC routing. Section VII proposes a Huazhong University of Science and Technology, Wuhan 430074, China proof-of-conceptinformation-releasing framework for practical (e-mail: [email protected]). use of the routing models. Section VIII analyzes the running Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. time and adaptability of the models. Finally, Section IX pro- Digital Object Identifier 10.1109/TITS.2015.2498186 vides concluding remarks. 1524-9050 © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 2614 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 17, NO. 9, SEPTEMBER 2016 II. RELATED WORK and carpool routing approaches were investigated to improve routing quality. Inspired by the honey bee foraging behavior, Routing deals with how to select proper paths for people, Wedde et al. presented a distributed and self adaptive vehicle vehicles, goods, and information. In this section, we intro- routing guidance approach based on a multi-agent system [26]. duce previous routing models and discuss the novelty of our In [27], the authors proposed a practical multi-agent based ap- approach. proach to achieve route allocation within a short time frame and In order to mitigate traffic congestion in transportation net- with low communication overheads. This work also demon- works, some dynamical routing models that incorporate real- strated the possibility to regulate system traffic using local time traffic information were recently proposed. In [14], an coordination strategies. In [28], a fuzzy inference technique optimal routing algorithm was developed with real-time traffic based approach for route guidance system was proposed by congestion information gathered through Geographic Infor- simultaneously using the information of road segments cost mation System (GIS). In [15], an adaptive vehicle routing and the information of overall O/D cost. In [29], an adaptive approach, which was formulated as a probabilistic dynamic multi-agent system based on the ant colony behavior and the programming problem and solved through a backward recur- hierarchical fuzzy model was proposed. The system allows sive procedure, was developed with real-time en route traffic efficiently adjusting the road traffic through an adaptive vehicle information to improve routing quality. In [16], the authors route guidance system. In [30], an ant colony routing algorithm presented a model predictive control framework for both cen- was developed for freeway networks to mitigate congestion and tralized traffic signal and route guidance systems aiming to reduce the number of vehicles near sensitive zones, such as minimize network congestion. In [17], system-optimal routing hospitals and schools. In the area of carpool routing guidance, problems were studied based on a reverse stackelberg game a carpool system was developed to coordinate ride matches and approach. Three schemes were proposed to induce drivers propose genetic-based carpool routes based on geographical, to follow routes that are computed to reach a system opti- traffic, and societal information [31]. In [32], an intelligent rout- mal distribution of traffic. Interdisciplinary approach was also ing scheme was proposed to provide many-to-many services investigated. Using methods from interacting polymers and with multiple pickup and dropping points. disordered systems, Yeung et al. [12] developed a simple, In this paper, with large-scale mobile phone data, we targeted principled, generic, and distributed routing algorithm capable travelers who experience the most severe congestion, and devel- of simultaneously considering all individual path choices. oped routing models with this information. The first advantage In order to find eco-friendly routes for drivers, several eco of the approach is that it pinpoints the key sources of conges- routing models were proposed to minimize fuel consumption tion,