Spatiotemporal Approach for Evaluating the Vehicle Restriction Policy with Multi-Sensor Data

Abstract

Vehicle restriction has been one of the most popular policies in alleviating traffic congestion and air pollution in mega cities. This paper evaluates the effect of the vehicle restriction policy on urban networks in Shanghai based on multi-source traffic data. First, before–after comparisons are conducted to investigate the impact of the restriction policy on travelers’ adaptation behaviors. A macroscopic fundamental diagram (MFD)-based approach is then applied to analyze the network performance under different levels of restrictions that have been implemented over the years. A spatial regression method is used to account for the spatial and temporal reasons of network performance under different restriction policy stages. Following the first increase of the vehicle restriction time in Shanghai (2015), the network-wide traffic performance significantly improved in the short term (within one month), but the effect was not substantial in the long term (one year). Affected drivers tended to shift either their travel routes or departure time. No significant increase of travelers on the public transport system was found after the extension of the vehicle restriction time. Both urban expressways and surface road networks became less efficient after a further extension of restricted time in 2016. Population distribution, interactions among different modes, distribution of parking facilities, and locations of ramps can account for the increase of congested links at the neighborhood level, which in turn aggravated the imbalance of the entire network after the extension of vehicle restriction time.

Urban traffic-parking system dynamics model with macroscopic properties: a comparative study between Shanghai and Zurich

“…The adoption of aggregate macroscopic fundamental diagrams (MFD) and collective network traffic analysis offers a comprehensive and cost-efficient solution to address this gap. Seminal works by Daganzo and Geroliminis (2008), Gu et al (2020), Geroliminis and Daganzo (2008), Huang et al (2021Huang et al ( , 2022aHuang et al ( , 2022b, Loder et al (2017), Zhao et al (2021), and Zhao et al (2022) have formulated and tested this paradigm shift, demonstrating its potential in effectively representing traffic system dynamics with fewer data needs and analytical complications. Recently, Cao and Menendez (2015) and Cao et al (2019) introduced a macroscopic model that incorporates MFD with dynamic phases, drawing inspiration from parking equilibrium concepts proposed by Anderson and De Palma (2004), Arnott et al (1991), andArnott et al (1993).…”