Grid Mapping for Spatial Pattern Analyses of Recurrent Urban Traffic Congestion Based on Taxi GPS Sensing Data

Abstract

Abstract:Traffic congestion is one of the most serious problems that impact urban transportation efficiency, especially in big cities. Identifying traffic congestion locations and occurring patterns is a prerequisite for urban transportation managers in order to take proper countermeasures for mitigating traffic congestion. In this study, the historical GPS sensing data of about 12,000 taxi floating cars in Beijing were used for pattern analyses of recurrent traffic congestion based on the grid mapping method. Through the use of ArcGIS software, 2D and 3D maps of the road network congestion were generated for traffic congestion pattern visualization. The study results showed that three types of traffic congestion patterns were identified, namely: point type, stemming from insufficient capacities at the nodes of the road network; line type, caused by high traffic demand or bottleneck issues in the road segments; and region type, resulting from multiple high-demand expressways merging and connecting to each other. The study illustrated that the proposed method would be effective for discovering traffic congestion locations and patterns and helpful for decision makers to take corresponding traffic engineering countermeasures in order to relieve the urban traffic congestion issues.

Global Optimality under Internet of Vehicles: Strategy to Improve Traffic Safety and Reduce Energy Dissipation

“…V L N ) function in Equation (4) are given by Equations (5) and (6). According to the calibration carried out by Helbing and Tilch [32], the optimal velocity (V(∆x n (t))) function and the ideal velocity (V(L/N)) function in Equation (4) are given by Equations (5) and (6).…”

“…In addition, β = 0.2 and the time interval is ∆t = 0.1 s [43]. The perturbation added to the above stable flow is ∆x 1 (0) = 5 m. Values of other parameters are the same as that in Equations (5) and (6). The evolution processes as well as the energy dissipation of traffic flow are simulated by varying the values of parameters λ and γ under different sensitivities.…”

“…Traffic problems such as traffic accidents, congestion, and energy consumption have aroused wide concern in modern society [1][2][3][4][5][6][7]. Intelligent Transportation System (ITS) is considered to have great potential for solving these problems [8][9][10][11][12][13][14].…”

Revealing Recurrent Urban Congestion Evolution Patterns with Taxi Trajectories

“…DBSCAN identifies random and non-convex shape clusters and determining the number of clusters in advance is not required [28,29]. We used Liu's [30] …”

“…The data from the 25 days were applied in the customized DBSCAN algorithm [30]. A total 13 recurrent congestion areas were discovered in Harbin second-ring road area at morning peak hours, which are shown in Figure 9.…”