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# How more development can lead to less travel: Examples

The 30-Minute City by David M. Levinson

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.

A Political Economy of Access: Infrastructure, Networks, Cities, and Institutions by David M. Levinson and David A. King