Modeling and Characterization of Traffic Flows in Urban Environments

Abstract

Currently, one of the main challenges faced in large metropolitan areas is traffic congestion. To address this problem, adequate traffic control could produce many benefits, including reduced pollutant emissions and reduced travel times. If it were possible to characterize the state of traffic by predicting future traffic conditions for optimizing the route of automated vehicles, and if these measures could be taken to preventively mitigate the effects of congestion with its related problems, the overall traffic flow could be improved. This paper performs an experimental study of the traffic distribution in the city of Valencia, Spain, characterizing the different streets of the city in terms of vehicle load with respect to the travel time during rush hour traffic conditions. Experimental results based on realistic vehicular traffic traces from the city of Valencia show that only some street segments fall under the general theory of vehicular flow, offering a good fit using quadratic regression, while a great number of street segments fall under other categories. Although in some cases such discrepancies are related to lack of traffic, injecting additional vehicles shows that significant mismatches still persist. Thus, in this paper we propose an equation to characterize travel times over a segment belonging to the sigmoid family; specifically, we apply logistic regression, being able to significantly improve the curve fitting results for most of the street segments under analysis. Based on our regression results, we performed a clustering analysis of the different street segments, showing that they can be classified into three well-defined categories, which evidences a predictable traffic distribution using the logistic regression throughout the city during rush hours, and allows optimizing the traffic for automated vehicles.

A Centralized Route-Management Solution for Autonomous Vehicles in Urban Areas

“…In this section, we describe the procedure followed to improve the traffic flow in the city of Valencia, Spain. In particular, we will focus on achieving the load balancing of vehicles by relying on Equation (1) presented in a previous work, which is able to adequately predict travel times depending on the degree of traffic congestion on a per-segment basis as shown in [7].…”

“…Also, we store the "edges", which are representations of streets or roads in the context of SUMO. These edges contain the identification code of the corresponding nodes, edge type information, the edge length, the vehicular speed associated with that edge, and the different street segments that have been unified, as presented in a previous work [7]. Figure 2b represents the scheme of the second phase of our smart interface.…”

“…We then relied on the DFROUTER tool [5], along with an iterative heuristic that refines the output produced by this tool, to obtain the Origin-Destination (O-D) traffic matrix that represents the internal trips inside the target area, and it resembles the real traffic distribution in the city [6]. The next step consisted of the characterization of travel times on a per-segment basis by applying a logistic regression [7].…”

Service-Oriented Cooperation Policies for Intelligent Ground Vehicles Approaching Intersections

“…With the ability of precise perception, the foundation of autonomous driving has been getting better and better. Especially, decision-making, autonomous control, path-planning and behavior policies are several vital aspects that have been studied and experienced [26][27][28][29][30][31]. For instance, Furda et al [32] studied a two-stage real-time decision-making method to improve road safety in city traffic systems, and then optimized the real-time decision-making method for autonomous city vehicles to select the most appropriate maneuver.…”

Estimation of Origin-Destination Flows of Passenger Cars in 1925 in Old Tokyo City, Japan

“…Studies using such data were reviewed by Yue et al (2014) [16]. Studies concerning vehicle counts from inductive loop sensors are also carried out [17,18]. However, these surveys have been conducted mostly after World War II, when these information technologies became available, and it is impossible to acquire people and traffic flows for the modern era.…”