﻿ 基于随机理论的公交路径选择建模 Modeling of Path Choice via Public Transit Based on the Random Theory

Modeling and Simulation
Vol.07 No.03(2018), Article ID:26649,9 pages
10.12677/MOS.2018.73021

Modeling of Path Choice via Public Transit Based on the Random Theory

Jiaxian Liang1*, Jiandong Qiu1, Jiajie Pan2

1Shenzhen Urban Transport Planning Center Co., Ltd., Shenzhen

2Traffic Information Engineering & Technology Research Center of Guangdong Province, Shenzhen

Received: Aug. 10th, 2018; accepted: Aug. 22nd, 2018; published: Aug. 29th, 2018

ABSTRACT

The paper presents the analysis of the stop choice and bus choice behaviors in space-time dimension using the public transit path choice model under the framework of random utility models, based on the idea of hierarchical information processing. The parameters of the perceived utility function of bus choice are calibrated and tested from TIYUZHONGXIN BRT stop to DONGPUZHEN BRT stop (8 direct routes) in Guangzhou with IC data and bus arriving data in case study. The result shows that waiting time, on-board time, comfortable at stop, comfortable at bus and the on-board experience are statistically significant in bus choice model under the hierarchical processing of degree of comfort.

Keywords:Transit Path Choice Models, Perceived Utility, Behavioral Information Processing, Parameter Calibration

1深圳市城市交通规划设计研究中心有限公司，深圳

2广东省交通信息工程技术研究中心，深圳

Copyright © 2018 by authors and Hans Publishers Inc.

This work is licensed under the Creative Commons Attribution International License (CC BY).

http://creativecommons.org/licenses/by/4.0/

1. 引言

2. 公交路径选择模型

Figure 1. Path example

2.1. 路径选择集

2.2. 路径选择机理

${V}_{s}\left[{\tau }_{d},t\right]={\beta }_{s}\cdot {X}_{s}+{{\beta }^{\prime }}_{s}\cdot {H}_{s}+{\beta }_{i_s}\cdot {X}_{i_s}+{\epsilon }_{s}$ (1)

${V}_{r}\ge {V}_{{r}^{\prime }},\forall {\tau }_{{r}^{\prime }}>{\tau }_{r}且r,{r}^{\prime }\in {R}_{s}\left[{\tau }_{r}\right]$ (2)

${V}_{r}={\beta }_{tb}\cdot T{B}_{r}+{\beta }_{tw}\cdot T{W}_{r}+{\beta }_{cf_s}\cdot C{F}_{s}+{\beta }_{cf_r}\cdot C{F}_{r}+{\beta }_{tf}\cdot T{F}_{r}+{\beta }_{i_l}\cdot {X}_{i_l}+{\epsilon }_{r}$ (3)

2.3. 学习过程

1) 恒定属性 ${X}^{c}$ ，属性值不随时间而改变，如换乘次数 $T{F}_{r}^{c}$ ，站点固定属性 ${X}_{s}^{c}$

2) 瞬时属性 ${X}^{te}$ ，这类属性是根据当前时刻的变化而更新，如候车时间 $T{W}_{r}^{te}$ ，站点舒适度 $C{F}_{s}^{te}$

3) 预测属性 ${X}^{p}$ ，这类属性可通过统计预测得到，如搭乘时间 $T{B}_{r}^{p}$ ，车内舒适度 $C{F}_{r}^{p}$ ，下一辆车舒适度 $C{F}_{{r}^{\prime }}^{p}$ ，公交站点吸引力 ${H}_{s}^{p}$

${V}_{s}\left[{\tau }_{d},t\right]={\beta }_{s}\cdot {X}_{s}^{c}+{{\beta }^{\prime }}_{s}\cdot {H}_{s}^{p}+{\beta }_{i_s}\cdot {X}_{i_s}^{te}$ (4)

${V}_{r}={\beta }_{tb}\cdot T{B}_{r}^{p}+{\beta }_{tw}\cdot T{W}_{r}^{te}+{\beta }_{cf_s}\cdot C{F}_{s}^{te}+{\beta }_{cf_r}\cdot C{F}_{r}^{p}+{\beta }_{tf}\cdot T{F}_{r}^{c}+{\beta }_{i_l}\cdot {X}_{i_l}^{te}$ (5)

2.4. 参数标定

$T{B}_{r}={t}_{r_{s}^{\prime }}^{l}-{t}_{r_s}^{l}$ (6)

$T{W}_{\mathrm{max}_r}={t}_{{r}^{\prime }_s}^{l}-{t}_{r_s}^{l},T{W}_{\mathrm{min}_r}={t}_{{r}^{\prime }_s}^{l\text{'}}-{t}_{r_s}^{l},其中l,{l}^{\prime }\in {K}^{s}\left[{\tau }_{r}\right]$ (7)

$C{F}_{r}=\frac{{N}_{\tau ,r}}{{N}_{c,r}},C{F}_{s}=\frac{{N}_{w,\tau ,s}}{{A}_{s}}$ (8)

${X}_{i_l}^{t}=\left\{\begin{array}{l}1,当前一次搭乘线路与本次一致时,\\ 0,其它.\end{array}$ (9)

3. 算例分析

Table 1. Accessible routes in the case study

Table 2. Attributes and parameters of bus choice models

4. 结论

Modeling of Path Choice via Public Transit Based on the Random Theory[J]. 建模与仿真, 2018, 07(03): 173-181. https://doi.org/10.12677/MOS.2018.73021

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