Research on impact of ride-hailing services on road traffic crashes in China

doi:10.16265/j.cnki.issn1003-3033.2024.02.0277

Abstract

Abstract:

To deeply explore whether ride-hailing services directly affect road traffic crashes and whether they indirectly affect road traffic crashes by changing the usage of buses and private cars, based on panel data from 114 cities in China from 2005-2017, the multi-period DID method and the causal steps method are used to estimate the impact mechanism of ride-hailing services on road traffic crashes. The results show that the entry of ride-hailing services increases road traffic crashes by 5.6%, and this conclusion still holds after performing robustness tests such as parallel trend and Propensity Score Matching-DID. Ride-hailing services significantly reduce public transit usage by about 19.3%, thus indirectly increasing road traffic crashes by 0.71%, which indicates that public transit usage plays a mediating effect. Ride-hailing services significantly increase private car usage by 5%, thus indirectly increasing road traffic crashes by 0.14%, which indicates that private car usage plays a mediating effect. The decrease in public transit usage is an important reason why ride-hailing services indirectly increase road traffic crashes. Government departments should strengthen the regulation of ride-hailing services, while encouraging the synergistic development of ride-hailing services and public transit.

Key words: ride-hailing services, road traffic crashes, public transit, mediating effect, multi-period difference-in-differences (DID)

CLC Number:

X951交通运输安全

WANG Zhuolun, LIN Yan. Research on impact of ride-hailing services on road traffic crashes in China[J]. China Safety Science Journal, 2024, 34(2): 209-216.

Figures/Tables 6

Tab.1

Descriptive statistics of variables

变量类别	变量定义	平均值	最小值	最大值	标准差
被解释变量	交通事故死亡人数ln(D_it)	4.971	1.386	7.461	0.903
解释变量	滴滴是否进入 $R i t$	0.132	0.000	1.000	0.339
中介变量	公交客运量ln(B_it)/×10⁴	9.156	1.974	13.612	1.546
中介变量	私家车保有量ln(E_it)/辆	11.816	7.844	15.452	1.376
控制变量	常住人口ln(P_it)/×10⁴	5.823	3.054	8.031	0.766
	国内旅游人数ln(T_it)×10⁴	7.173	2.827	10.487	1.189
	人均国内生产总值ln(G_it)/元	10.338	8.232	12.320	0.772
	人均工资ln(W_it)/元	10.427	9.252	11.813	0.480
	失业率U_it/%	3.474	1.100	7.600	0.780
	人均道路面积ln(A_it)/m²	2.579	0.438	4.321	0.528
	降雨量ln(F_it)/mm	6.340	3.616	7.971	0.910
	医院数ln(H_it)/个	4.485	2.639	6.613	0.800

Tab.1

Tab.2

Regression results of ride-hailing services affecting road traffic crashes

变量	$l n (D i t)$
$R i t$	0.070 0^* (0.036 8)	0.055 8^* (0.033 7)
$l n (P i t)$	—	0.471 0^*** (0.148 0)
$l n (T i t)$	—	0.050 0^* (0.027 5)
$l n (G i t)$	—	-0.112 9^** (0.051 7)
$l n (W i t)$	—	-0.007 9 (0.116 0)
$U i t$	—	-5.416 5^*** (1.721 1)
$l n (A i t)$	—	0.029 5 (0.043 8)
$l n (F i t)$	—	-0.009 0 (0.033 6)
$l n (H i t)$	—	0.052 3^* (0.031 0)
常数项	5.308 9^*** (0.022 0)	3.394 8^** (1.437 7)
时间固定效应	是	是
城市固定效应	是	是
观测值	1 482	1 482
R²	0.342 6	0.375 7

Tab.2

Fig.1

Tab.3

PSM-DID robustness test results

变量	$l n (D i t)$
$R i t$	0.079 8^** (0.038 0)	0.067 3^* (0.037 7)
常数项	5.308 4^*** (0.022 2)	3.525 0^** (1.477 2)
控制变量	否	是
时间固定效应	是	是
城市固定效应	是	是
观测值	1 45 4	1 45 4
R²	0.345 3	0.378 5

Tab.3

Tab.4

Results of mediating effects of bus passenger volume and private car ownership

变量	列1	列2	列3	列4
变量	$l n (B i t)$	$l n (D i t)$	$l n (E i t)$	$l n (D i t)$
$R i t$	-0.192 6^*** (0.054 4)	0.048 7^* (0.024 7)	0.050 0^* (0.029 1)	0.054 3^* (0.032 3)
$l n (B i t)$	—	-0.036 8^* (0.019 1)	—	—
$l n (E i t)$	—	—	—	0.028 6^* (0.016 3)
常数项	5.336 3^** (2.470 1)	3.5913^** (1.421 6)	3.2189^** (1.537 7)	3.3083^** (1.453 4)
控制变量	是	是	是	是
时间固定效应	是	是	是	是
城市固定效应	是	是	是	是
观测值	1 48 2	1 48 2	1 48 2	1 48 2
R²	0.445 5	0.396 3	0.602 3	0.479 6

Tab.4

Tab.5

Sobel test and Bootstrap test

中介变量	Sobel-Goodman	Z值	P值	Bootstrap 抽样次数	95%置信区间
中介变量	Sobel-Goodman	Z值	P值	Bootstrap 抽样次数	下限	上限
$l n (B i t)$	Sobel	-1.937	0.052 7	500	-0.057 7	-0.002 4
	Goodman-1	-1.931	0.053 4	1 000	-0.058 3	-0.001 7
	Goodman-2	-1.943	0.052 0	1 500	-0.056 6	-0.003 4
$l n (E i t)$	Sobel	3.987	0.000 1	500	0.034 1	0.081 9
	Goodman-1	3.974	0.000 1	1 000	0.033 0	0.083 1
	Goodman-2	4.001	0.000 1	1 500	0.034 1	0.082 0

Tab.5

References 22

[1]	每日经济新闻. 2021城市交通出行报告[EB/OL]. [2023-06-15]. https://new.qq.com/rain/a/20210418A09YLI00.
[2]	中国互联网络信息中心. 第49次中国互联网络发展状况统计报告[EB/OL]. [2023-06-14]. https://www.cnnic.cn/n4/2022/0401/c88-1131.html.
[3]	ERHARDT G D, HOQUE J M, GOYAL V, et al. Why has public transit ridership declined in the United States?[J]. Transportation Research Part A: Policy and Practice, 2022, 161: 68-87. doi: 10.1016/j.tra.2022.04.006
[4]	BANSAL P, SINHA A, DUA R, et al. Eliciting preferences of TNC users and drivers: evidence from the United States[J]. Travel Behaviour and Society, 2020, 20:225-236. doi: 10.1016/j.tbs.2020.04.002
[5]	左盛丹. 深圳网约车交通事故率高于出租车[EB/OL]. [2023-06-15]. https://www.chinanews.com.cn/auto/2017/06-02/8239874.shtml.
[6]	DILLS K, MULHOLLAND E. Ride-sharing, fatal crashes, and crime[J]. Southern Economic Journal, 2018, 84(4): 965-991. doi: 10.1002/soej.v84.4
[7]	GREENWOOD N, WATTAL S. Show me the way to go home: an empirical investigation of ride-sharing and alcohol related motor vehicle homicide[J]. Mis Quarterly, 2017, 41(1): 163-187. doi: 10.25300/MISQ
[8]	MORRISON N, MEHRANBOD C, KWIZERA M, et al. Ridesharing and motor vehicle crashes: a spatial ecological case-crossover study of trip-level data[J]. Injury Prevention, 2021, 27: 118-123. doi: 10.1136/injuryprev-2020-043644 pmid: 32253258
[9]	BARRIOS J M, HOCHBERG Y V, YI H. The cost of convenience: ridesharing and traffic fatalities[J]. Journal of Operations Management, 2023, 69(5): 823-855. doi: 10.1002/joom.v69.5
[10]	KIRK S, CAVALLI N, BRAZIL N. The implications of ridehailing for risky driving and road accident injuries and fatalities[J]. Social Science & Medicine, 2020, 250(4): DOI: 10.1016/j.socscimed.2020.112793.
[11]	艾媒网. 网约车行业数据分析:2019年中国网约车客运量占出租车客运量的37.1%[EB/OL]. [2023-06-15]. https://www.iimedia.cn/c1061/71939.html.
[12]	GUO Yue, XIN Fu, BARNES J, et al. Opportunities or threats: the rise of online collaborative consumption (OCC) and its impact on new car sales[J]. Electronic Commerce Research and Applications, 2018, 29: 133-141. doi: 10.1016/j.elerap.2018.04.005
[13]	清华大学社科院. 2021年中国一线城市出行平台调研报告[EB/OL]. [2023-06-15]. http://www.199it.com/archives/1246525.html.
[14]	TURNER D, ALLSOP E. Road casualties and public transport fares in London[J]. Accident Analysis & Prevention, 1986, 18(2):147-156. doi: 10.1016/0001-4575(86)90059-X
[15]	宝驾租车. 国内首份重点城市“留守汽车”数据发布[EB/OL]. [2023-06-16]. http://www.12gang.com/article_10634.html.
[16]	LI Xin, WU Liyu, YANG Xianfeng. Exploring the impact of social economic variables on traffic safety performance in Hong Kong: a time series analysis[J]. Safety Science, 2018, 109: 67-75. doi: 10.1016/j.ssci.2018.05.010
[17]	温忠麟, 叶宝娟. 中介效应分析:方法和模型发展[J]. 心理科学进展, 2014, 22(5):731-745. doi: 10.3724/SP.J.1042.2014.00731
	WEN Zhonglin, YE Baojuan. Analyses of mediating effects: the development of methods and models[J] Advances in Psychological Science, 2014, 22(5): 731-745. doi: 10.3724/SP.J.1042.2014.00731
[18]	YAO Di, XU Liqun, ZHANG Chunqin, et al. Revisiting the interactions between bus service quality, car ownership and mode use: a case study in Changzhou, China[J]. Transportation Research Part A: Policy and Practice, 2021, 154: 329-344. doi: 10.1016/j.tra.2021.10.017
[19]	MACKINNON P, LOCKWOOD M, WILLIAMS J. Confidence limits for the indirect effect: distribution of the product and resampling methods[J]. Multivariate Behavioral Research, 2004, 39(1): 99-128. doi: 10.1207/s15327906mbr3901_4 pmid: 20157642
[20]	李杰, 曾叙砜, 孙领, 等. 全国道路交通安全水平的时空布局演化研究[J]. 中国安全科学学报, 2021, 31(12):136-143. doi: 10.16265/j.cnki.issn1003-3033.2021.12.018
	LI Jie, ZENG Xufeng, SUN Ling, et al. Research on temporal and spatial evolution of road safety in China[J]. China Safety Science Journal, 2021, 31(12): 136-143. doi: 10.16265/j.cnki.issn1003-3033.2021.12.018
[21]	路庆昌, 崔欣, 徐标, 等. 公交接驳场景下轨道交通网络脆弱性研究[J]. 中国安全科学学报, 2021, 31(8):141-146. doi: 10.16265/j.cnki.issn1003-3033.2021.08.020
	LU Qingchang, CUI Xin, XU Biao, et al. Vulnerability research of rail transit network under bus connection scenarios[J]. China Safety Science Journal, 2021, 31(8): 141-146. doi: 10.16265/j.cnki.issn1003-3033.2021.08.020
[22]	朱彤, 秦丹, 魏雯, 等. 基于机器学习的公交驾驶员事故风险识别及影响因素研究[J]. 中国安全科学学报, 2023, 33(2):23-30. doi: 10.16265/j.cnki.issn1003-3033.2023.02.0034
	ZHU Tong, QIN Dan, WEI Wen, et al. Research on accident risk identification and influencing factors of bus drivers based on machine learning[J]. China Safety Science Journal, 2023, 33(2): 23-30. doi: 10.16265/j.cnki.issn1003-3033.2023.02.0034