Losers and Pareto optimality in optimising commuting patterns

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.