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The Dynamic Optimization of the Departure Times of Metro Users During Rush Hour in an Agent-Based Simulation: a Case Study in Shenzhen, China

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The Dynamic Optimization of the Departure Times of Metro Users During Rush Hour in an Agent-Based Simulation: a Case Study in Shenzhen, China applied sciences Article The Dynamic Optimization of the Departure Times of Metro Users during Rush Hour in an Agent-Based Simulation: A Case Study in Shenzhen, China Yuliang Xi 1,2, Qingyun Du 1,2,*, Biao He 3, Fu Ren 1,2, Yu Zhang 1,2 and Xinyue Ye 4 ID 1 School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China; [email protected] (Y.X.); [email protected] (F.R.); [email protected] (Y.Z.) 2 Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University, Luoyu Road 129, Wuhan 430079, China 3 College of Architecture and Urban Planning, Shenzhen University, Nanhai Street 3688, Nanshan District, Shenzhen 518061, China; [email protected] 4 Department of Geography, Kent State University, Kent, OH 44242, USA; [email protected] * Correspondence: [email protected]; Tel.: +86-27-6877-8842; Fax: +86-27-6877-8893 Received: 13 September 2017; Accepted: 18 October 2017; Published: 25 October 2017 Abstract: As serious traffic problems have increased throughout the world, various types of studies, especially traffic simulations, have been conducted to investigate this issue. Activity-based traffic simulation models, such as MATSim (Multi-Agent Transport Simulation), are intended to identify optimal combinations of activities in time and space. It is also necessary to examine commuting-based traffic simulations. Such simulations focus on optimizing travel times by adjusting departure times, travel modes or travel routes to present travel suggestions to the public. This paper examines the optimal departure times of metro users during rush hour using a newly developed simulation tool. A strategy for identifying relatively optimal departure times is identified. This study examines 103,637 person agents (passengers) in Shenzhen, China, and reports their average departure time, travel time and travel utility, as well as the numbers of person agents who are late and miss metro trips in every iteration. The results demonstrate that as the number of iterations increases, the average travel time of these person agents decreases by approximately 4 min. Moreover, the latest average departure time with no risk of being late when going to work is approximately 8:04, and the earliest average departure time with no risk of missing metro trips when getting off work is approximately 17:50. Keywords: dynamic optimization; departure times; metro; rush hour; agent-based simulation; Shenzhen 1. Introduction With the development of national economies and the acceleration of urbanization, traffic congestion has become a common problem around the world [1,2]. Traffic congestion produces broad social and environmental costs, such as delays in the delivery of goods, air pollution, changes in microclimates, increases in the urban heat island effect, increases in respiratory problems and lost productivity [3]. To address this severe challenge, intelligent transportation system (ITS)-based studies have become increasingly prevalent, especially in the field of traffic simulation. Traffic simulations, which represent an important component of ITSs and differ from traditional mathematical modelling, can simulate traffic-related phenomena, including the flow of traffic and traffic accidents. They can also reproduce the spatial and temporal variations in traffic flow and help to analyze the characteristics of vehicles, drivers, pedestrians, roads and traffic [4,5]. Moreover, using virtual reality technology, traffic simulations can intuitively show the status of traffic in real time, including whether roads are Appl. Sci. 2017, 7, 1102; doi:10.3390/app7111102 www.mdpi.com/journal/applsci Appl. Sci. 2017, 7, 1102 2 of 17 smooth, whether traffic accidents are present and what kind of solutions can be used to address traffic problems, without risk. Traffic simulations can be divided into macroscopic, microscopic and mesoscopic scales [6]. The macroscopic traffic simulation model was proposed by Whitham and Richards and is mainly used to study the characteristics of traffic flow globally [7,8]. Microscopic traffic simulations, such as the car-following model (Reuschel, Pipes) and the lane-changing model (Gipps, 1986), can describe the conditions of traffic networks and the detailed behaviors of traffic participants to a high degree [9–11]. The description of traffic systems provided by the mesoscopic traffic simulation model proposed by Prigogine and Andrews lies between those of the macroscopic and microscopic models. The mesoscopic model describes the inflow and outflow behaviors of vehicles at links and intersections or at links and stops, but it does not include car-following or lane-changing models [12]. In terms of application, macroscopic and mesoscopic traffic simulations are both used for traffic planning and management; mesoscopic traffic simulations can also be used to present travel suggestions to the public, whereas microscopic traffic simulations can be used for traffic accident analysis and automated driving. Despite the different scales at which traffic simulation research is conducted, most traffic simulation tools are based on multi-agent models, which are a powerful tool for the simulation of complex systems. Such a model is dynamic and captures nonlinear interactions, and its components exhibit unpredictable reactions that result in realistic behavior patterns [13–15]. Agent technology originated at MIT in the 1970s. Agents are typically defined as independent and autonomous entities that can coordinate with each other according to the different states of other entities and systems and determine their own actions and states [16]. Various kinds of elements, such as people, vehicles, links, and traffic lights, have been viewed as agents. These elements communicate and collaborate to simulate complex traffic conditions in simulation tools. Among the many simulation tools that exist today, our research focuses on those that are concerned with resident trips. MATSim (Multi-Agent Transport Simulation), Albatross and ADAPTS (Agent-based Dynamic Activity Planning and Travel Scheduling) are typical examples of these simulation tools [17–19]. Most traffic simulation tools that are concerned with resident trips use activity-based models to identify optimal combinations of activities in time and space [20]. However, it is also necessary to carry out commuting-based traffic simulations, especially for public transit. The key element of the simulation tools mentioned above is the replanning module, which employs the scores and plans of the agents and determines whether these travel plans must be modified. Most optimization algorithms for travel planning focus on four dimensions, specifically the departure time, travel route, travel mode and the combinations of activities represented by the activity chain [21]. The optimization of the departure time contains two steps. The first of these steps involves setting a range of time adjustments based on the current departure time. The second step involves drawing an adjustment value at random from within the range and adding this adjustment to the previous departure time (Balmer et al., 2005; Gao, 2013) [2,22]. The optimization of the travel route is based on static traffic assignment using the user equilibrium (UE) or the stochastic user equilibrium (SUE) models (Raney et al., 2003) [23]. The multinomial logit model and the probit model are often used to optimize the mode of travel, and the optimization of the combinations of activities represented by the activity chain is based on heuristic solution algorithms, such as hill-climbing algorithms or the tabu search algorithm (Balmer and Axhausen, 2009; Nagel and Axhausen, 2016) [20,21,24,25]. Of the four types of optimization algorithms, research on the optimization of departure time is still insufficient. The challenges involved in such studies are as follows. First, it is difficult to define the direction of adjustment of the departure time, i.e., whether the optimal departure time is earlier or later than the current departure time. Second, it is also difficult to know when the iteration can be stopped. In this study, iteration refers to the replanning process in our simulation tool. Person agents must replan their travel strategies—i.e., perform departure time adjustment—to avoid late arrival or missing their desired metro trips. If there are no person agents who are late when going to work or who become caught in congestion at metro stops when getting off work, the iteration will end. Appl. Sci. 2017, 7, 1102 3 of 17 In view of the two challenges identified above, this paper aims to explore a strategy for adjusting departure times to obtain relatively optimal travel utility values, using metro travel as an example. To address the first problem, the two concepts of “obligatory arrival times” and “empirical travel times” are introduced to determine the direction of departure time adjustment. For the second problem, an explicit condition is proposed for the termination of iteration. With these adjustments, shorter travel times can be achieved. Moreover, we find the latest average departure time with no risk of being late when going to work and the earliest average departure time with no risk of missing metro trips when getting off work. This research on the optimization of departure times will broaden the horizons in this field and can be used to provide the public with suggestions regarding their travel plans. 2. Methodology This section first presents a brief overview of the general simulation approach used in MATSim and then introduces our self-developed multi-agent
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