﻿ 基于停车信息的城市交通流量预测 Urban Traffic Flow Prediction Based on Parking Information

Open Journal of Transportation Technologies
Vol. 07  No. 06 ( 2018 ), Article ID: 27644 , 8 pages
10.12677/OJTT.2018.76048

Urban Traffic Flow Prediction Based on Parking Information

Yongjie Xing1, Mingzhe Liu1, Wenhao Li1, Jing Dong2

1School of Architecture and Transportation, Guilin University of Electronic Technology, Guilin Guangxi

2Scientific Research Institute of Traffic Police Detachment, Guilin Public Security Bureau, Guilin Guangxi

Received: Oct. 31st, 2018; accepted: Nov. 13th, 2018; published: Nov. 20th, 2018

ABSTRACT

In order to make effective use of parking information, this article uses sparking information acquisition system to obtain vehicle in and out data. On this basis, the “four-phase method” of traffic planning providing by TransCAD is applied to establish a macroscopic traffic planning model, and the dynamic traffic flow running state of the surrounding roads is predicted backwards. Finally, an urban traffic flow prediction method based on parking information is proposed, and it is proved that this method can effectively predict regional road network traffic flow.

1桂林电子科技大学建筑与交通工程学院，广西 桂林

2桂林市公安局交警支队科研所，广西 桂林

1. 引言

2. 交通四阶段法

3. 基于停车信息的城市交通流量预测建模

Figure 1. Forecasting process

3.1. 出行分布

${q}_{ii}=K{O}_{i}^{\alpha }{D}_{i}^{\beta }{S}_{i}^{\gamma }$ (1)

$\alpha$$\beta$$\gamma$ 为待定系数

${q}_{ij}={A}_{i}{O}_{i}{B}_{j}{D}_{j}f\left({c}_{ij}\right)$ (2)

${A}_{i}=\frac{1}{\underset{j}{\sum }{B}_{j}{D}_{j}f\left({c}_{ij}\right)}$ (3)

${B}_{j}=\frac{1}{\underset{j}{\sum }{A}_{i}{O}_{i}f\left({c}_{ij}\right)}$ (4)

3.2. 交通分配

${p}_{ki}^{\omega }=\frac{{e}^{-{\theta }_{i}{c}_{k}^{\omega }}}{\underset{r\in {R}_{\omega }}{\sum }{e}^{-{\theta }_{i}{c}_{k}^{\omega }}},\forall k\in {R}_{\omega },\omega \in W,i\in I$ (5)

${q}_{\omega i}={D}_{\omega i}\left({C}_{\omega i}\right)\le {\stackrel{¯}{q}}_{\omega i},\forall \omega \in W,i\in I$ (6)

${C}_{\omega i}\left({c}_{\omega }\right)=E\left[\mathrm{min}\left(\underset{k\in {R}_{\omega }}{{C}_{ki}^{\omega }}\right)|{c}_{\omega }\right]=-\mathrm{ln}\underset{k\in {R}_{\omega }}{\sum }{e}^{-{\theta }_{i}{c}_{k}^{\omega }}/{\theta }_{i},\forall \omega \in W,i\in I$ (7)

${f}_{ki}^{\omega }={q}_{\omega i}{p}_{ki}^{\omega },\forall k\in {R}_{\omega },\omega \in W,i\in I$ (8)

4. 案例分析

4.1. 计算过程

1) 交通小区划分

Table 1. Traffic cell data structure table

Figure 2. Schematic diagram of traffic cell division

2) 建立路网

Table 2. Road network data structure table

3) 交通分布

Table 3. Study area travel OD matrix (1 - 10 cells)

4) 交通流分配

Figure 4. Road section flow saturation map

Figure 5. Flow rate of intersections at part of the study area

4.2. 结果分析

Table 4. Comparison of traffic volume prediction and distribution in some sections

5. 结语

Urban Traffic Flow Prediction Based on Parking Information[J]. 交通技术, 2018, 07(06): 397-404. https://doi.org/10.12677/OJTT.2018.76048

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