Built environment and potential job accessibility effects of road pricing: A spatial econometric perspective

Abstract

This paper analyzes the impacts of the built environment (BE) as it relates to the potential job accessibility (PJA) effects of road pricing. The relationships between the BE elements and PJA under a road charging policy are established using a spatial econometric approach, which uses an integrated land use and transportation model (TRANUS model) and a spatial lag model (SLM). With the intent of further analyzing the differences in the PJA effects of road pricing on traffic analysis zones (TAZs) that contain different combinations of BE elements, a quantitative classification method combining factor and cluster analysis is applied. This will quantitatively categorize TAZs inside and outside the tolled areas. In exploring the relationship between changes in PJA and the road pricing policy, we found the spatial autocorrelation coefficient to be negative. This result suggests that we are unable to increase the PJA of all the regions through road pricing, but rather affect a redistribution of PJA between different regions. Results also indicate that the impacts of road charging on PJA are associated with urban BE elements. Moreover, such effects are the common result of specific characteristics of the BE. The higher the number of jobs, the better the public transportation conditions, and the better the street design (high densities of street and intersections), the less the region will be negatively influenced by a road charging policy, and vice versa. To avoid the negative effects of road pricing on PJA prior to the launch of such a policy, cities should improve public transportation networks and enhance the street design of the road pricing policy areas, especially the toll ring periphery area.

Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility

“…Thus, the implementation of SAVs alone makes the job accessibility within the toll ring increase the most. The results of the effect of road congestion pricing (Scenario C) is in line with the existing study (Zhong & Bushell, 2017a). • An interesting point to note is that compared with implementing one technology alone, the combination of introducing SAVs and road congestion pricing makes the job accessibility in all TAZs reduced, which indicates that the two technologies restrict each other.…”

“…• For the road congestion pricing scenario (Scenario C), the number of jobs and population within the toll ring will decline to some extent due to the increase in travel costs. This indicates that the road congestion pricing policy makes the region spread outward and inhibits the development within the toll ring, which is consistent with the previous research results (Zhong et al, 2015;Zhong & Bushell, 2017a). Specifically, compared with the trend development scenario (Scenario A), when the road congestion fee is 20 CNY, the rate of changes in the number of jobs and population within the toll ring will decline to 97.22% and 80.11% by 2040, respectively.…”

“…• For areas outside the toll ring, the impact of road congestion pricing (Scenario C) on regional job accessibility is affected by the built environment attributes of each TAZ. Besides, the impact on TAZs with different built environment attributes is not the same, which is consistent with the results of Zhong and Bushell (2017a).…”

“…Mobility refers to the ease of movement, while accessibility refers to the ease of arriving at activities. The former reduces the travel cost per mile of travelers, while the latter reduces the cost of travelers arriving at each activity (Levine & Garb, 2002;Zhong & Bushell, 2017a). It has been proved that the improvement of mobility brought by cars does not necessarily improve the accessibility (Ewing, Schmid, Killingsworth, Zlot, & Raudenbush, 2008).…”

“…Job accessibility is defined as the ease of reaching a job at a location by using a range of transportation modes (Geurs & van Wee, 2004). The indicator consists of two parts, including a weighted variable that describes the number of jobs in a given area and an impedance variable associated with travel time that measures the difficulty of reaching a job from a certain location (Zhong & Bushell, 2017a). Generally speaking, the closer the area to a large number of jobs, the greater the regional job accessibility, and vice versa.…”