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This paper outlines new indicators for evaluating the probable impacts of introducing different land use/transport policies on the commuting efficiency of a city. It uses Beijing as a case study to describe how smartcard data can be used to derive a large number (n = 216,884, 9% of the population) of bus commuters' workplace and residential locations. Using existing excess commuting indicators and new commuting efficiency gain indicators established to assess policy options, it exemplifies how to assess impacts of different policies on bus commuting efficiency gains. The case study indicates policies that directly target bus commuters (such as BRT) bring greater commuting efficiency gains when compared to other policies such as car usage restrictions and stringent travel demand management measures in the downtown area. [ABSTRACT FROM AUTHOR]

When optimising the overall commuting pattern for a city or a region, there are often winners and losers among commuters at the subdivision level. Losers are those who are burdened with longer commutes than before the optimisation. Knowing who or where losers are is of interest to both researchers and policy-makers. The information would help them efficiently locate losers and compensate them. Few, however, pay attention to such losers. By revisiting ‘excess commuting’ in the economic framework, we show that optimising the commuting pattern is comparable to restoring Pareto optimality in commuting. Using Beijing as a case study, we identify and geo-visualise the losers when the city’s bus commuting pattern is optimised. We examine the severity of the loss among the losers, the spatial pattern of the losers and their influencing factors. We find that most losers are located around the epicenter. The severity of the loss is independent of jobs/housing ratio but is associated with the commute distance before the optimisation. Workers whose commute distance is less than the global average are more likely to become losers. Places where losers reside have significantly lower employment density in a few industries than where non-losers reside. A low jobs/housing ratio in individual subareas does not necessarily increase the average trip length of commuters therein. A low jobs/housing ratio of one or several subareas, however, could influence the average trip length of all the commuters in the area. Locating diverse jobs and housing opportunities around or along transit corridors could compensate the losers and reduce the overall commuting cost. [ABSTRACT FROM AUTHOR]

Using Beijing as an example, this research demonstrates that smartcard data can be used to (a) assemble the required data for excess commuting studies, and (b) visualise related results. Based on both smartcard and household travel survey data, we find that the theoretical minimum commute is considerably lower for bus users than for car users in Beijing. This suggests that there is a greater inter-mixing of jobs–housing functions (i.e., a better jobs–housing balance) associated with users of that mode compared to the corresponding land-use arrangement for car users, who locate further from the central area (Tian’anmen) than bus users. The commuting range for car users is 9.4 km greater than for bus users. Excess commuting is slightly higher for bus users (69.5%) than for car users (68.8%). Commuting capacity values are slightly lower for car users than for bus users, implying that car users consume less of their available commuting resources overall than bus users, albeit only marginally. [ABSTRACT FROM AUTHOR]

The article focuses on a study aimed at mapping the commuting with cellular network data for solving problem of traffic jam in Shanghai, China. It mentions need for quantifying and visualizing the location of commuters where they reside and work. It states various solutions for dealing with traffic jam such as increasing the carrying capacity of the transportation system, reducing number of commuting trips and distributing commuting trips across modes of travel, space and time.

This manuscript analyzes personal and carsharing consumption characteristics of alternative commuters of the same university in Los Angeles, California. The paper finds that: (1) one can achieve a reasonably high carsharing rate among university employees, particularly employees commuting by alternative modes other than driving alone, female employees, and employees whose income is lower than the median income of all the employees; (2) university employees' participation rate in the carsharing program is lower than the students' and thus, the latter contributes more to the existing argument that a university campus is a niche market for carsharing; (3) commuter benefits are not only correlated to the employee participation rate of a carsharing program, but also to the behavior of the participants: their frequency and quantity of carsharing consumption and when to carshare; (4) free hours do not necessarily increase the carsharing consumption of employee carsharers who are eligible for these hours; (5) one does not need a large number of shared vehicles to satisfy the needs of employee carsharers at the place of work. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 and subsequent pandemic containment measures have significantly affected our daily life, which has been extensively examined in the existing scholarship. However, the existing scholarship has done little on the jobs/housing relationship impacts of COVID-19. We attempted to fill this gap by using an excess-commuting approach. The approach allows us to analyse a series of jobs-housing matrices based on the location-based service big data of around fifty million individuals in the Pearl River Delta (PRD), China before and amid COVID-19. In the PRD, a zero-COVID policy was implemented, which presents a distinct and interesting context for our study. We found that after the COVID-19 outbreak: (1) residences and employment became more centrally located in downtowns, which is opposite to the suburbanization trend elsewhere; (2) in the whole PRD, the minimum and maximum commutes became smaller while the actual commute became larger, indicating the simultaneous presences of some paradoxical phenomena: a better spatial juxtaposition of jobs and housing, more compressed distribution of jobs and housing, and longer average actual commutes; (3) inter-city commutes between large cities were significantly refrained and decreased, while new inter-city commuters between smaller cities emerged; (4) it was more likely for the less-educated and female workers to see smaller minimum commutes amid COVID-19. This paper illustrates the potential of big data in the longitudinal study on jobs-housing relationships and excess commuting. It also produces new insights into such relationships in a unique context where stringent anti-COVID-19 policies have been continuously in place. [ABSTRACT FROM AUTHOR]

Commuting efficiency, a measure of how effectively workers utilise their time and resources for journeys to work, is generally assessed through an excess commuting framework. This approach quantifies the disparity between the theoretical minimum and actual commutes. However, conventional methods often make oversimplified assumptions of industry homogeneity and temporal invariance, neglecting the intricate dynamics of commuting efficiency. To bridge the gap, this study scrutinises cross-year commuting efficiency across 13 industry sectors by developing a disaggregated excess-commuting framework. An analysis of substantial data encompassing 11 million commuters in Shenzhen over the period from 2017 to 2021 reveals that: (1) secondary sectors and industries requiring relatively lower skills demonstrate higher commuting efficiency compared to their tertiary, high-skilled counterparts; (2) increases in industry-related minimum and random commuting significantly contribute to the growth of commuting distances, whereas local self-contained employment helps mitigate this effect; and (3) addressing jobs-housing imbalance in absolute ratio may not necessarily reduce commuting distances for all industry sectors. Urban policy development should be specifically tailored to the unique evolution of commuting efficiency performance within individual industries, rather than adopting a generic one-size-fits-all approach. [ABSTRACT FROM AUTHOR]