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.
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.
This paper describes the connection between stop spacing and person-weighted accessibility for a transit route. Population distribution is assumed to be uniform along the line, but at each station, demand drops with distance from the station. The study reveals that neither short nor excessive stop spacings are efficient in providing accessibility. For the configuration of each transit route, an optimum stop spacing exists that maximizes accessibility. Parameters including transit vehicle acceleration, deceleration, top speed, dwell time, and pedestrian walking speed affect level of accessibility achiev- able, and differ in their effect on accessibility results. The findings provide an anchor of reference both for the planning of future transit systems, and for transit operators to make operational changes to system design parameters that improve accessibility in a cost-effective manner. The study technically justifies the “rule of thumb” in setting different stop spacings for metro, streetcars, and other different transit services. Different types of transit vary in their ability to provide accessibility, slower moving streetcar (tram) type urban rails are inherently disadvantaged in that respect. Thus the type of transit service to be built should be of particular concern, if the transit is to effectively serve its intended population.
Why does Australia have higher transit use than the US?
This question has two major explanations: Driving is harder and using transit is easier. On the road side, as my colleague Wes Marshall says: “Policy-related differences include stronger and more extensive enforcement programs [in Australia], restrictive licensing programs, and higher driving costs.”
In places like central Sydney, narrower lanes and expensive parking also make driving a burden. The Australian motorway system is less developed than the US interstate highway system, though the government is funding major new urban motorways in Australia (e.g. WestConnex in Sydney).
Transit benefits because higher population and employment density (especially around transit stations ) within cities compared to most US cities (as well as a more urban population overall) reduces access time to and from transit and enables higher frequency service to serve the demand. The train, bus, and tram systems in Australian cities are relatively high frequency and fairly reliable, with all-day service. While the systems are imperfect (as any daily commuter will tell you) they are orders of magnitude better than most of the US.
Transit service is a positive feedback system (The Mohring Effect, named for Transport Economist Herb Mohring who first identified it). More demand calls for more service, the additional service is in the form of additional buses and trains running at different times than the original service, reducing schedule delay, making transit more convenient, calling for more service. This works two ways, so transit cutbacks increase headways (decrease frequency) making transit less convenient, lowering demand, resulting in more cutbacks.
From the 1920s when tram (streetcar) use peaked (notably excepting the spike during World War II) through the 1960s there was a process of Bustitution — substitution of buses for trams. Many cities around the world (notably excepting places like Melbourne, Toronto, San Francisco, and especially selected cities in Europe) instead of paying the costs of recapitalising their tram systems, opted to convert tram lines to buses that had much lower capital costs.
In the US, there is a grand conspiracy theory, about how this came about. While most of the conspiracy theory is over-blown, there was some evil doing, as is the wont of people infected with greed (better known as people). In Minneapolis the people who converted the streetcar to buses went to jail, not for the conversion but for crimes like bribing state legislators and giving kickbacks. In Brisbane, the Paddington tram depot caught (were set) on fire as bus conversion was being debated, answering the question.
In general, the reality is much more market-rational. Electric trams were first deployed in the late 1880s, so by 1950 the service was over 60 years old. Trams needed a major capital infusion to keep operating. That capital infusion was not forthcoming from fares; in the US trams had clearly been in decline for the better part of thirty years. It was a hard call for cities not to replace their trams with buses. The private sector, which financed trams initially, were unwilling to finance it again, leaving it to local governments to come up with money for the trams (or not, as it turned out).
So most cities became tramless. Those cities were losing transit riders before the conversion and lost more after the conversion. It’s a vicious cycle.
The new Light Rail mode (See Appendix) in North America kicked off with Edmonton (1978), San Diego (1981), and Portland (1986). In retrospect, many people regret the process of bustitution, and cities that later reinstalled LRT systems would with perfect foresight likely have kept their tram lines going and recapitalised them. Note that the actual coverage of these new system is much smaller than the historical trams, most tram lines were removed in most North American cites, as in Sydney.
Wikipedia reports the farebox recovery is lower in Australia than US cities, which implies a higher public subsidy. (I am not convinced there aren’t methodological differences in accounting here, but it is worth noting).
Why is Australia’s transit use rising when the US is falling?
The second question is more difficult. One response is that fuel prices remain higher in Australia. Another is that there has been more investment in transit, including more frequent service and continuous improvements to stations and vehicles. Third, Australian cities have recently rolled out smart cards (Opal in NSW) like the Oyster Card in London, and along with it pricing reforms to reduce the fare penalty for transfers, which has significantly boosted use of transit.
Australia does some other things differently from the US. Among them is increased use of contracting out to private firms to provide service. (This is not universal yet, but is growing.) This is also done in the UK and most of Europe, but not very much in the US. This has effects on costs and perception and unionization. The contractors are for-profit businesses aligned with the idea of higher ridership, so support for transit in Australia is bipartisan, while in the US, transit is considered a Democrat issue in most places, and Republicans are often actively hostile as it is not their constituents (or only support transit to their suburban districts with high cost, low value commuter rail systems like Northstar in Minnesota).
While transit in the US is perpetually in “crisis” (to listen to its supporters), in Australia (and Canada and Europe) it is a normal part of society that is widely used, and doesn’t have the same stigma associated with it.
What should the public sector do to increase ridership?
I asked on Twitter “Would restoring Sydney Trams to a network resembling that at their maximum extent (291 km), similar in scope to Melbourne’s Tram network today, be a good use of public resources?”
The response was
50% Yes, Benefits >> Costs
27% No, Benefits << Costs
23% Maybe, Benefits ~= Costs
Looking at Sydney the densities are much higher here than in most North American cites, aside from New York, San Francisco, Chicago. I previously examined the existing and planned trams in Sydney.
Because they are widely used, they have a strong constituency for their betterment, and government is responsive in expanding the system.
Convincing existing some-time riders to ride more is far easier than going from 0 to 1 as Peter Thiel might say.
I think early ridership gains come from going deep rather than going wide. A large fraction of the US still lives in areas designed around transit (basically pre-1920 America), including city cores and streetcar and commuter rail suburbs. Residents sometimes use transit now. These places are much easier to serve because the land use in conducive to transit, the densities are high enough and the networks are oriented for transit access and service.
Good, relatively cost-effective service like Minnesota’s arterial BRT (bus rapid transit ) (MetroTransit’s A Line, eg) have shown large ridership and user satisfaction gains with low investment. The system is made more efficient with things like payment before boarding, and all-door boarding, reducing time at stops and increasing driver and bus productivity.
The aim should be to serve users better, not help non-users by reducing congestion, which may be a happy byproduct, any more than building roads aims to reduce transit crowding.
Wesley E. Marshall (2018) “Understanding international road safety disparities: Why is Australia so much safer than the United States?” Accident Analysis and Prevention 111. 251–265
Mohring, H.(1972). “Optimization and Scale Economies in Urban Bus Transportation,” American Economic Review, 591-604.
Appendix: Streetcars and Trams vs. LRT
The difference between Light Rail and older streetcars or trams is primarily, but not entirely, one of branding. Anyone who says there is a clear formal difference that people abide hasn’t gotten out much. Different cities use the same words to mean different things. Still there are differences in degree:
Streetcars or trams often share right-of-way in the street, while Light Rail often has a mostly exclusive right-of-way with at-grade crossings, but either system can be operated either way.
Light Rail vehicles tend to be wider with higher capacity and longer with higher capacity, its longer vehicle is a heavier vehicle: Light Rail is not light, it’s only light with respect to commuter trains; it’s not light with respect to buses, cars, or people. Light sounds airy and like it should be less expensive, but it’s a only a little less expensive.
Transit vehicles and services form a continuum, you can operate streetcars in exclusive tunnels as in Boston. Both LRT and streetcars differ from commuter trains but it’s a continuum in regard to that as well.
Alexandria is a neighbourhood (and once an independent municipality) south of Sydney on the new City and Southwest Metro Line, which is slated to open in 2024. There are stations at Sydenham to the west and at Waterloo on the east but nothing in-between for Alexandria. The area has a population density of 1540/km^2, which is plausible for public transport, and is going up with new construction. This is the second longest stretch on the under construction Metro line without a station (only Epping to Cherrybrook is longer).
Should Alexandria get a stop? On the one hand, more stops increases running time for all on-board passengers. On the other, it lowers access costs for those locally who otherwise would need to walk a longer distance or take a bus.
Let’s consider a hypothetical: If we say an extra 1 minute for the stop, it is adding the number of passengers traveling through the station each day (~30,000) x 1 minute each. (It is hard to quickly track down current ridership numbers, I have seen estimates of about 40,000 per day on the T3 line, but not all of them will go past this point .. the Metro will increase capacity, and may increase ridership, and development will drive in that direction anyway.) So if it were to Board 3000 people who saved 5 minutes each way (boarding and alighting) in travel cost compared to their next best alternative, the total amount of time lost would be equivalent to the time saved. (It’s of course more complicated than this, as existing riders may switch stations as well, and changing mode has implications at both ends of trips.) I am pulling these number out of thin air to illustrate the logic, an actual demand analysis could estimate their actual values (recognizing the inaccuracies of demand forecasting). It is not obvious that it would pencil out from a time-savings perspective, i.e. adding 3000 boardings and 3000 alightings to the station per day is a significant amount, even with the new development. This analysis does not even consider the cost of the additional stop, which is far from free. Nevertheless, sometimes the need of the one outweigh the needs of the many.
If it gets a stop where would it be?
Given the map and assuming the line’s location does not move, I would say at the southern edge of Alexandria, somewhere along Sydney Park, probably at Mitchell Road so it can be near the huge new Park Sydney development (technically in Erskineville). It might make sense to be connected to the St. Peter’s Station for ease of transfers.
It is also worth noting that Alexandria is going to feel the brunt of the WestConnex exit to Euston Road / McEvoy at the St. Peters Interchange.
The local neighbourhood group, ARAG, is lobbying for a station, as they should. The reluctance to an Alexandria Station they have heard from government agencies is the lack of redevelopable land in Alexandria to justify a station. That is, new stations are built to serve undeveloped sites rather than to serve proven demand. The same reasoning was given to route the line to Waterloo rather than University of Sydney in the first place. This seems strange on both accounts. The University of Sydney is growing like gangbusters, and even if existing homes were off-limits, there is plenty of redevelopable industrial land in Alexandria, mostly to the east of the circle on the map I drew. But in any case, the test should be in providing accessibility, and existing land use has as much right to that as greenfield (or brownfield) development. If the tax structure and regulatory system were rational (for instance, used a land value tax), it should not matter whether the new riders were from existing or new developments.
This study conducts an in-depth analysis to alert policymakers and practitioners to erroneous results in the positive impacts of transit use on health measures. We explore the correlation of transit use and accessibility by transit and walking with self-reported general health, Body Mass Index (BMI), and height. We develop a series of linear regression and binary logit models. We also depict the coefficient-p-value-sample-size chart, and conduct the effect size analysis to scrutinize the practically significant impacts of transit use and accessibility on health measures. The results indicate transit use and accessibility by transit and walking are significantly associated with general health and BMI. However, they are practically insignificant, and the power of the large sample in our particular case causes the statistically insignificant variable to become significant. At a deeper level, a 1% increase in transit use at the county level diminishes the BMI by only 0.0037% on average. The elasticity of transit use also demonstrates that every 1% increase in transit use would escalate the chance of having excellent or very good general health by 0.0003%. We show there is a thin line between false positive and true negative results. We alert both researchers and practitioners to the dangerous pitfalls deriving from the power of large samples and the weakness of p-values. Building the results on just statistical significance and sign of the parameter of interest is worthless, unless the magnitude of effect size is carefully quantified post analysis.
Cities and their transportation systems become increasingly complex and multimodal as they grow, and it is natural to wonder if it is possible to quantitatively characterize our difficulty to navigate in them and whether such navigation exceeds our cognitive limits. A transition between different searching strategies for navigating in metropolitan maps has been observed for large, complex metropolitan networks. This evidence suggests the existence of another limit associated to the cognitive overload and caused by large amounts of information to process. In this light, we analyzed the world’s 15 largest metropolitan networks and estimated the information limit for determining a trip in a transportation system to be on the order of 8 bits. Similar to the “Dunbar number,” which represents a limit to the size of an individual’s friendship circle, our cognitive limit suggests that maps should not consist of more than about 250 connections points to be easily readable. We also show that including connections with other transportation modes dramatically increases the information needed to navigate in multilayer transportation networks: in large cities such as New York, Paris, and Tokyo, more than 80% of trips are above the 8-bit limit. Multimodal transportation systems in large cities have thus already exceeded human cognitive limits and consequently the traditional view of navigation in cities has to be revised substantially.
My take is this greatly supports things like Grid networks and network simplification (see the work of Jarrett Walker). This looked at rail. Think about buses. In a few years, people will just let their apps navigate them, and human cognition limits may fall off the chart.
The overall results: “Perceived and actual wait times are clearly related, but the relationship is variable,” Guthrie says. “The waiting environment can change perceptions.”
Nearly 85 percent of those surveyed waited 10 minutes or less. Even with waits under a minute, however, people tended to perceive at least a minute or two, and they tended to estimate in round numbers (5, 10, 15 minutes). “This creates an initial ‘penalty’ of overestimates,” he says.
Researchers also found several variables to have statistically significant impacts. The presence of a shelter—even a simple one—made waits seem shorter, especially for waits less than 10 minutes. “The biggest difference in perception was between any shelter and none at all,” he says. The presence of a NexTrip real-time information sign also shortened perceived waits.
Posted schedules produced a “really interesting pattern,” Guthrie says. For shorter waits, schedules caused people to overestimate wait time, but after about 10 minutes, people began to underestimate it. “It’s possible that for short waits, people compare the clock and the schedule and get impatient, but for longer waits, they are reassured to know the bus or train is coming,” Guthrie says. “This implies that posting schedules is more important for routes with less frequent service.”
Gender alone was not significant, but there was a stark difference for women in less safe environments. “Most sites in the study were rated as safe, but at those that were not, there’s potential to improve the experience for riders and potential riders,” he says.
“With several major initiatives currently under way to expand the number of shelters at bus stops and to improve the quality of transit schedule information across our entire network, the timing of this project could not be better,” says Marilyn Porter, director of engineering and facilities for Metro Transit. “This study provides important insight that is directly applicable to the work that we are doing to ensure that our customers have the best possible experience using transit service in the Twin Cities.”
The model developed in the project includes many other variables such as household income, trip purpose, and the presence of benches and route maps. “Users of the model will be able to choose criteria and predict the impacts of hypothetical feature mixes,” Guthrie says.
A final report is planned for publication in March. Humphrey School associate professor Yingling Fan was the study’s principal investigator; David Levinson, RP Braun/CTS Chair in the Department of Civil, Environmental, and Geo- Engineering, was co-investigator.
Public policy for mass transit in the United States is largely focused on a few modes of travel: commuter rail, urban rail, urban bus, and paratransit requirements. These few modes certainly carry most of the transit riders in the country, but do not represent a full understanding of the breadth of options that are required to make a truly transit-oriented city. New York is the most transit-oriented city in North America, and it is likely that when most people not from New York think about transit in New York they think about the subway system, or perhaps they include iconic yellow cabs or remember that there are a lot of buses. If you ask most New Yorkers, they will probably add many other modes, but even then there will likely be many modes left out.
An underappreciated reason why New York functions so well as a transit-oriented city—and can grow transit ridership without new expansion of core services (yet)—is that there are oodles of transit options available. Mode choices for travelers is not a binary choice between driving and transit, even though this has been the general attitude toward transit policy over the past few decades. Observing travel in New York suggests just how complex the required systems are to actually provide meaningful alternatives to automobility.
Below are 33 different categories of mass transit offering regular service in New York City (I have reviewed this list with native New Yorkers but I am sure others will have constructive comments about my categories). This is what it takes to create transit-orientation for a city, and I suspect many of these exist in cities everywhere but planners and scholars are not aware of them. In New York lots and lots of operators offer many different services to many different types of people. Not all technologies work for all places, so transit technologies should reflect the problems to be solved.
New Jersey Transit buses
Metro-North Rail Road
Long Island Rail Road
New Jersey Transit trains
Staten Island Ferry
Staten Island Rail Road
Commuter ferries (Five licensed operators)
Access-a-Ride (MTA and other transit provider contracts)
Yellow taxicabs (Medallion cabs)
Green taxicabs (Boro cabs)
Liveries for Hire (Uber, Lyft, Carmel, etc.)
Commuter vans (licensed and pre-arranged fares; e.g. Mario’s Transportation)
Dollar vans and local jitneys (informal immigrant services)
Chinatown buses (intercity)
Low cost intercity buses (Bolt Bus, Mega Bus)
Conventional intercity buses (Greyhound, Peter Pan)
Apartment shuttles (CoSo, etc.)
University shuttles (Columbia University, New York University)
New Jersey commuter jitneys
Long Island commuter jitneys
Roosevelt Island Tram (Gondola)
Roosevelt Island Red Bus (Publicly owned development corporation)
CitiBike bike share (public access for a fee)
University bike share programs (free access for a designated group)
When planning local transportation systems we now commonly say that multiple modes are required. We underestimate how many modes this is and how challenging it is to accommodate everything. Each of these 33 categories represents different customers, fare policies, public/private ownership, terminal capacity, vehicles, road access, curbside access, infrastructure needs, etc. Most of these different types of transit are regulated under municipal or state laws, too, and require the allocation of public space (roads and waterways) more than large-scale capital investment. I outlined some of these challenges in a recent CityLab piece.
The main takeaway from this is that for transit to be useful it must reflect the many ways people need to get around the city. Multi-modal transport doesn’t mean cars-transit-bikes-pedestrians. There are multiple modes of transit, too.
Suppose you have a train moving along (parallel to) an East-West (EW) signalized arterial.
Case 1: If the signals are pre-timed, and the timings are known in advance, the train should never have to stop for the signals (aside from emergency signal pre-emptions and other edge cases). Instead, the train should be able to adjust its speed so that it doesn’t have to stop. It might go at an average speed of say 10, 20, 30, or 40 MPH in order to ensure it hits a green light or better a green wave from whenever it departs a station. The train driver can be apprised of the optimal time to leave the previous (upstream) station, and the speed to travel to hit “green” lights.
Green waves have been around since the 1920s (See Henry Barnes’s autobiography: The Man with the Red and Green Eyes. Dutton. 1965. OCLC522406). Static signs to tell travelers the speed of the green wave has been in standard use in some places (e.g. Connecticut Avenue in Washington, DC) for almost as long. Dynamic real-time signs which tell travelers what speed to adjust to to make the green wave has been recently patented and tested in simulation for automobiles: Always Green Traffic Control. The time is ripe for some carefully controlled field experimentation.
Still, pre-timing with information certainly doesn’t guarantee the fastest speed possible for the train, but it does guarantee no stops except at stations, which is good for a variety of reasons, including both travel time (avoid acceleration/deceleration loss), traveler comfort, energy use, and train wear and tear.
Case 2: If the signals are actuated, that is, their phase and perhaps cycle timings depend on traffic levels, and traffic “actuates” the signal, usually through an in-ground loop detector, transit signal priority from a fixed upstream distance should be sufficient to ensure the train doesn’t stop at a “red” light. The traffic light controller would know that a train was coming, and either keep the lights in the direction of the train green (if they are green), or change them to green and hold them, if it is currently red and the green is coming up. The train, knowing when the green will be on, should be able to adjust its speed (faster or slower) to make the green without stopping.
The distance that trains can currently notify a downstream signal controller is when they depart the upstream station, which is up to 1/2 mile or so (the spacing between stations). 1/2 mile at 30 mph takes 1 minute. With a cycle time of 2 minutes, and at least half the green time (1 minute) for the signalized arterial, a green can be guaranteed. If the light is currently red, it will be green within a minute. If it is currently green, it can be kept green for up to a minute. The worst case is it was just about to turn red and instead the green is extended for an additional minute. Alternatively, if it is currently green, a shorter than usual red phase can be inserted to clear the crossing traffic, before the light is turned back to green.
For traffic signals less than 1/2 mile downstream (say 1/4 mile) the warning time is only 30 seconds at 30 MPH. The same logic applies, but it is potentially more problematic as there is less lead time to adjust the timings, so the phase shortenings might be more severe. On the other hand, if more than 50% of the green time goes to the EW movement (say 75%) you aren’t really any worse off.
At 1/10 of a mile the warning time is less, but train departure from the station should be able to be coordinated with the light directly.
Case 3: But let’s say your traffic engineers are incapable of making this work. Should the train and its passengers suffer? This is where traffic signal pre-emption comes in. Most widely used for emergency vehicles, this potentially changes the sequence of phases, so maybe a phase is dropped (it doesn’t occur within the cycle, or within the usual place in the cycle).
This system does ensure that the vehicle requesting the pre-emption gets a green light as quickly as possible (safely turning the conflicting movements to a red phase) and thus can drive at as high a speed as possible. While trains should not need to stop at traffic lights with priority and speed adjustments, with pre-emption, they neither need to stop nor adjust their speed.
What could go wrong?
Pedestrians. Thus far we have been talking about a system with cars and trains. Pedestrians too can actuate signals, though “beg buttons“. These may function similar to vehicle actuators, in telling the traffic signal there is someone who wants to cross. The difficulty for priority or pre-emption is that a pedestrian phase may need to be longer since pedestrians take longer to cross the street than a vehicle does, especially if the street is very wide. So a pedestrian actuator may also extend the green time, in addition to calling for green time. This makes it more difficult to quickly change lights from red to green, since for safety reasons you can’t strand a pedestrian. This makes the ability to adjust train speeds in concert with the traffic signals more important.
Emergency vehicles. Emergency vehicle on emergency vehicle crashes are a known problem, and pre-emption may make it worse as firetrucks approaching a scene from two directions may both demand a green light, but only one gets it. The driver of one vehicle, not realizing he didn’t get the green (especially if he had the green as he was approaching), fails to yield. There are solutions to these problems.
Any of this will likely lead to additional delays for conflicting vehicle movements (cars making left turns or North-South traffic crossing our East-West arterial). With priority, this may even lead to extra delay for some vehicles on the parallel arterial who have been given a short green so the conflicting traffic can also get a short green before the EW arterial returns to green.
However the train usually has more people on it than are queued up at the other directions, so total *person* delay will generally be reduced.
For a variety of reasons, delay is bad (unless your goal is punishing drivers and air-breathers), we want to minimize total person time (or weighted total person time – recognizing long weights are more onerous than short weights) in the system (because time is money), and minimize pollution outcomes as well.
In short, the Green Line not getting green lights on University Avenue is a solvable problem. It should have been solved already. It eventually will be solved.