We evaluated the ratio of jobs to workers from Smart Card Data at the transit station level in Beijing.
A year-to-year evolutionary analysis of job to worker ratios was conducted at the transit station level.
We classify general cases of steepening and flattening job-worker dynamics.
The paper finds that only temporary balance appears around a few stations in Beijing.
Job-worker ratios tend to be steepening rather than flattening from 2011 to 2015.
Abstract
As a megacity, Beijing has experienced traffic congestion, unaffordable housing issues and jobs-housing imbalance. Recent decades have seen policies and projects aiming at decentralizing urban structure and job-worker patterns, such as subway network expansion, the suburbanization of housing and firms. But it is unclear whether these changes produced a more balanced spatial configuration of jobs and workers. To answer this question, this paper evaluated the ratio of jobs to workers from Smart Card Data at the transit station level and offered a longitudinal study for regular transit commuters. The method identifies the most preferred station around each commuter’s workpalce and home location from individual smart datasets according to their travel regularity, then the amounts of jobs and workers around each station are estimated. A year-to-year evolution of job to worker ratios at the station level is conducted. We classify general cases of steepening and flattening job-worker dynamics, and they can be used in the study of other cities. The paper finds that (1) only temporary balance appears around a few stations; (2) job-worker ratios tend to be steepening rather than flattening, influencing commute patterns; (3) the polycentric configuration of Beijing can be seen from the spatial pattern of job centers identified.
Jie Huang, David Levinson, Jiaoe Wang, Jiangping Zhou, and Zi-jia Wang (2018) Tracking job and housing dynamics with smartcard data (Open Access) PNAS published ahead of print November 19, 2018 [doi]
Significance
This paper uses transit smartcards from travelers in Beijing retained over a 7-y period to track boarding and alighting stations, which are associated with home and work location. This allows us to track who moves and who remains at their homes and workplaces. Therefore, this paper provides a longitudinal study of job and housing dynamics with group conceptualization and characterization. This paper identifies four mobility groups and then infers their socioeconomic profiles. How these groups trade off housing expenditure and travel time budget is examined.
Abstract
Residential locations, the jobs–housing relationship, and commuting patterns are key elements to understand urban spatial structure and how city dwellers live. Their successive interaction is important for various fields including urban planning, transport, intraurban migration studies, and social science. However, understanding of the long-term trajectories of workplace and home location, and the resulting commuting patterns, is still limited due to lack of year-to-year data tracking individual behavior. With a 7-y transit smartcard dataset, this paper traces individual trajectories of residences and workplaces. Based on in-metro travel times before and after job and/or home moves, we find that 45 min is an inflection point where the behavioral preference changes. Commuters whose travel time exceeds the point prefer to shorten commutes via moves, while others with shorter commutes tend to increase travel time for better jobs and/or residences. Moreover, we capture four mobility groups: home mover, job hopper, job-and-residence switcher, and stayer. This paper studies how these groups trade off travel time and housing expenditure with their job and housing patterns. Stayers with high job and housing stability tend to be home (apartment unit) owners subject to middle- to high-income groups. Home movers work at places similar to stayers, while they may upgrade from tenancy to ownership. Switchers increase commute time as well as housing expenditure via job and home moves, as they pay for better residences and work farther from home. Job hoppers mainly reside in the suburbs, suffer from long commutes, change jobs frequently, and are likely to be low-income migrants.
Balancing housing and jobs, so that they are located near each other, logically reduces travel compared to a situation where those same jobs are far apart. This has long been understood in the transport planning community (see e.g. Cervero 1989, or my 1998 paper), but is not well grasped among the general public.
However, moving a fixed number of things around is not how cities actually grow. Telling place A you taking away their employment is controversial. More generally new things are added.
Development in Mascot. Photo by author.
It is commonly asserted that more development adds to congestion. And often this is true. But not always, it depends on the type of development. More housing in a housing-rich and job-poor area will result in more total travel. More employment in a job-rich, housing poor area will do similarly. More housing in a job-rich area, and more jobs in a housing-rich area can actually reduce travel.
For our baseline case, imagine a city with two precincts separated by 2 km.
Precinct A: 1000 Jobs, 0 Resident Workers
Precinct B: 0 Jobs, 1000 Resident Workers.
The one-way (morning commute) trip table looks like:
Jobs
1000
0
Workers
A
B
0
A
0
0
1000
B
1000
0
Total daily travel to work is 2000 person km per day. (Everyone commutes from B to A). Travel on Link BA is 1000 at 2 km per trip, or 2000 person km traveled. (This just analyzes one-way trips. Round trip commutes would double this.)
Case 1.
There is a proposal to intensify development in Precincts A and B, so each is more locally balanced.
Precinct A: 1000 Jobs, 500 Resident Workers
Precinct B: 500 Jobs, 1000 Resident Workers.
The new one-way (morning commute) trip table looks like (rounded):
Jobs
1000
500
Workers
A
B
500
A
498
2
1000
B
503
497
assuming 0.5 km intrazonal travel distance, using a doubly-constrained gravity model with a d_{ij}(-2)impedance function.
The Daily Travel on links:
AB = 2 @ 2 km
BA = 503 @ 2 km
within A = 498 @ 0.5 km (walking)
within B = 497 @ 0.5 km
TOTAL = 1507 pkt.
This is considerably less than the baseline case as many more travelers can reach their destinations locally. While there is still some commuting, it is far less than before.
Case 2.
There is a proposal to build a locally-balanced Precinct C halfway between Precincts A and B.
Precinct C has 500 Jobs and 500 Workers
The new one-way (morning commute) trip table looks like:
Jobs
1000
0
500
Workers
A
B
C
0
A
0
0
0
1000
B
666.666667
0
333.333333
500
C
333.333333
0
166.666667
assuming 0.5 km intrazonal travel distance, using a doubly-constrained gravity model with a d_{ij}(-2)impedance function.
The Daily Travel on links:
BC = BA + BC = 1000 @ 1 km
CA = BA + CA = 1000 @ 1 km
within C = 166 trips @ 0.5 km
TOTAL = 2083 pkt.
In this example, the total person kilometers traveled (pkt) on the links connecting inter-city precincts is essentially identical to the base case, despite adding 500 residents and 500 workers halfway between each. There are an additional 167 pkt daily on the intrazonal market (within C), which is likely walking.
The total one-way commute travel per person however drops, from 2 km/person per day to about 1.38 km/person per day. The average trip length is reduced. The experienced travel is thus about one-third lower.
Case 3
Building on Case 1, completely balancing A and B (so each has 1000 jobs and 1000 workers) reduces one-way commutes further (to 1176 pkt)
The new one-way (morning commute) trip table looks like (rounded):
Jobs
1000
1000
Workers
A
B
1000
A
941
59
1000
B
59
941
assuming 0.5 km intrazonal travel distance, using a doubly-constrained gravity model with a d_{ij}(-2)impedance function.
So, it should be clear from this example that adding development can actually reduce total travel, if it is the right kind of development in the right places.
This article: Region’s Job Growth a Centrifugal Force starts badly “As a consensus builds that the Washington region needs to concentrate job growth, there are signs that the exact opposite is happening.” and gets worse.
David Levinson, Transportist 
You must be logged in to post a comment.