Investigating Travel Flow Differences between Peak Hours with Spatial Model with Endogenous Weight Matrix Using Automatic Vehicle Identification Data

Abstract

The rapid urbanization has brought great challenges to the transportation network. However, travel flow at peak hours is not always the same. It is important to investigate how travel flow differs between peak hours to capture travel flow patterns and influential factors to facilitate traffic management and urban planning. This paper establishes a spatial model with endogenous weight matrix (SARBP-EWM) to investigate the travel flow differences between morning and evening peaks on both weekday and weekend based on automatic vehicle identification (AVI) data and point of interest (POI) data in Xuancheng, China. The results confirm strong spatial effects and endogeneity issue. Besides, facility variables such as number of offices and number of clinics reveal strong negative impacts on travel flow differences on both weekday and weekend, while the number of middle school shows significantly positive relation with travel flow differences. In addition, the endogenous weight matrix on both weekday and weekend is successfully estimated and compared. It is found that TAZ pairs tend to be clustered with lower spatial weights on weekday, while they are more randomly distributed with higher spatial weights at weekend. Based on the results above, the policies proposed from Xuancheng 14th Five-Year Plan are evaluated and discussed. The above empirical analysis quantifies impacts from key factors on urban travel flow differences between peak hours and provides important references for urban planning and policy making.

Analyzing urban crash incidents: An advanced endogenous approach using spatiotemporal weights matrix

“…• Population (Almasi & Behnood, 2022;Alves et al, 2021;Feizizadeh et al, 2022;Huang et al, 2016;Mahmud et al, 2019;Wu et al, 2024;Zhou et al, 2022) • Median household income (Huang et al, 2019) • Land use (Almasi & Behnood, 2022;Feizizadeh et al, 2022;Sandoval-Pineda et al, 2022;Wang, Yuan, et al, 2019) • POI (Almasi & Behnood, 2022;Sandoval-Pineda et al, 2022;Wang, Yuan, et al, 2019;Zhou et al, 2022) • Street level (Alves et al, 2021;Feizizadeh et al, 2022;Huang et al, 2016) • Zonal level (Almasi & Behnood, 2022;Huang et al, 2016Huang et al, , 2019Mahmud et al, 2019;Sandoval-Pineda et al, 2022;Wang, Yuan, et al, 2019;Wu et al, 2024;Zhou et al, 2022) Weather data • Precipitation, snow depth, temperature, wind speed, visibility, cloud cover (Alves et al, 2021;Hasan et al, 2022) • Street level (Alves et al, 2021) • Zonal level (Hasan et al, 2022) According to Retting et al (1999) while VMT measures the total miles driven by vehicles within a particular area or over a specific period. Furthermore, running red lights, by either drivers or pedestrians (Retting et al, 1999), can increase crash rates.…”

“…Socioeconomic factors are also critical to understanding the causes of car accidents. Prior research has revealed that population, employment rates, and the number of uneducated residents (Almasi & Behnood, 2022;Alves et al, 2021;Feizizadeh et al, 2022;Huang et al, 2016;Mahmud et al, 2019;Wu et al, 2024;Zhou et al, 2022), as well as median household income (Huang et al, 2019), correlate with pedestrian accidents. In addition, specific land use and points of interest, such as hospitals and schools, have been shown to affect traffic accidents in TAZs, according to studies conducted by Almasi and Behnood (2022) Some studies mentioned in Table 2 focus on constructing models for macro-scale accident prediction, using TAZ as the unit of analysis.…”

“…Recently, Zhou et al. (2022) employed a spatial autoregressive binary model with an endogenous SWM to examine the variances in travel flow between morning and evening peak times on weekdays and weekends. They developed an SWM that quantifies the relative weights among all TAZ pairs based on endogenous variables.…”

“…, Feizizadeh et al (2022),Sandoval-Pineda et al (2022),Wang, Yuan, et al (2019) andZhou et al (2022) Hasan et al (2022). andAlves et al (2021) have also suggested that weather conditions like rain, snow, frost, and fog can negatively impact accidents due to decreased visibility and difficulty controlling a vehicle.…”

The Impacts of High-Speed Railway on Urban GDP and Its Agglomeration: Evidence from China

“…Tus, the panel data model aids in identifying the efects of time trends, observed individual diferences, and endogeneity, thereby providing a more comprehensive understanding and quantifcation of the impact of projects such as the economic externalities of HSR [30,31]. Failure to capture the impact of policies at a certain time node is a drawback of the panel data model [15,32]. Spatial econometric models focus on the interrelationships of infuencing factors within geographic space [6,35].…”