Built environment, peak hours and route choice efficiency: An investigation of commuting efficiency using GPS data

Abstract

Commuting efficiency measures the impact of commuting on urban spatial and social environments. Urban policies aim to reduce commuting distances and improve commuting efficiency by supporting mixed land-use and job-housing balance. Many studies have addressed these issues by examining excess commuting at the aggregate level, and most aggregated measurements of excess commuting are based on home-to-work commuting flows between zones. However, residents' travel behaviour does not consist solely of rational attempts at minimizing commuting distances but is instead affected by commuting hours, the complexity of the commuting chain and the built environment. Thus, commuting efficiency must be studied at the individual or disaggregated levels. This paper examined individual commuting efficiency in suburban Beijing using a GPS-facilitated activity-travel survey and investigated the differences in commuting distance and route choice efficiency between morning and evening peaks. Notably, in so doing, we considered non-work stops, and also explored the impact of urban spatial factors-in both residential and working areas-on commuting efficiency using multilevel mixed effects generalized linear models. The findings suggest that there are significant differences in commuting distance and commuting efficiency between morning and evening peaks based on the nature of commutes. Residents working in city centres or with jobs near railway stations, with higher road network densities or with lower facility densities seem to be more adept at selecting optimal routes.

Mapping and Quantifying Spatial and Temporal Dynamics and Bundles of Travel Flows of Residents Visiting Urban Parks

“…Another clear finding is that the temporal patterns of clusters of travel flows were significantly related to spatial factors, which is consistent with the findings of most studies that human movement is regular, both spatially and temporally [4,7,35]. This finding makes it possible to manage residents' travel routes based on their routine travel to parks.…”

“…However, sometimes park visitors may not actually follow the shortest path [21]. For example, travelers often take detours to avoid traffic jams, or because they have incomplete information about transport options and conditions [35]. Some authors have also found that visitors select travel routes based on travel time rather than the shortest path [21,36].…”

“…Travelling to urban parks, is fact, not only related to space and time constraints of travel routes, but also spatial-physical characteristics of the parks and socio-demographic factors [38], such as available park facilities, population density around the parks, visitors' gender and household structure [5,35]. Understanding such physical and non-physical drivers may be important for addressing critical travel flows for recreational use of urban parks.…”

A Balance Interface Design and Instant Image-based Traffic Assistant Agent Based on GPS and Linked Open Data Technology

“…Fridman et al [27] used styles, applications, web browsing, and GPS positioning to realize the related technologies of active authentication on mobile devices. Based on the survey of GPS-assisted travel in Beijing suburbs, Ta et al [28] studied the related technologies of personal commuting efficiency according to the difference in commuting distance and route selection efficiency between the morning and evening peak traffic hours. Aliprantis et al [29] described a concept with image identification and matching from the Europeana platform, which can link the LOD cloud from cultural institutes around Europe and mobile augmented reality applications for cultural heritage without accurate geo-based locations.…”

Congestion impacts of shopping using vehicle tracking data

“…Previous regulations about commercial activities such as shopping centres, retail trade, restaurants and offices in residential areas were relaxed to accommodate the pressures for these amenities, resulting in a mixed land use. While such mixed land-use is encouraged to reduce traffic (Ta et al 2016), residential location choice in Dhaka is often based on school ing of children, and not the commute distances or local amenities (Choudhury and Ayaz 2016), which calls into question the advantages of mixed land use. Especially, the proliferation of unplanned shops and shopping centres throughout the city is thought to be a major contributor to congestion.…”