New U.S. light rail transit starter systems — Comparative total costs per mile

LEFT: LA Blue Line train emerging from tunnel portal. (Photo: Salaam Allah.) RIGHT: Norfolk Tide LRT train on single-track railroad roght-of-way. (Photo: Flickr.)

LEFT: LA Blue Line train emerging from tunnel portal. (Photo: Salaam Allah.) RIGHT: Norfolk Tide LRT train on single-track railroad right-of-way. (Photo: Flickr.)

This article has been updated to reflect a revision of the LRN study described. The study was revised to include Salt Lake City’s TRAX light rail starter line, which was opened in late 1999.

What’s been the been cost per mile of new U.S. light rail transit (LRT) “starter systems” installed in recent years?

The Light Rail Project team was curious about this, so we’ve reviewed available data sources and compiled a tabulation comparing cost-per-mile of “heavy-duty” LRT starter systems installed in or after 1990, all adjusted to 2014 dollars for equivalency. (“Heavy-duty” distinguishes these systems from lighter-duty streetcar-type LRT projects.)

This is shown in the figure below, which presents, for each system, the year opened, the initial miles of line, the cost per mile in millions of 2014 dollars, and comments on significant construction features. (“RR ROW” refers to available railroad right-of-way; “street track” refers to track embedded in urban street pavement, almost invariably in reserved lanes or reservations.)


Major data sources have included TRB/APTA 8th Joint Conference on Light Rail Transit (2000), individual LRN articles, and Wikipedia.

Averaging these per-mile cost figures is not meaningful, because of the wide disparity in types of construction, ranging from installation of ballasted open track in railroad right-of-way (lowest-cost) to tunnel and subway station facilities (highest-cost). These typically respond to specific conditions or terrain characteristics of the desired alignment, and include, for example:

Seattle — While Seattle’s Link LRT is by far the priciest system in this comparison, there are explanatory factors. Extensive modification of existing Downtown Seattle Transit Tunnel (and several stations) previously used exclusively by buses; tunneling through a major hill, and installation of a new underground station; extensive elevated construction to negotiate hilly terrain, major highways, etc.

Dallas — This starter system’s costs were pushed up by a long tunnel beneath the North Central Expressway (installed in conjunction with an ongoing freeway upgrade), a subway station, a new viaduct over the Trinity River floodplain, and significant elevated construction.

Los Angeles — The Blue Line starter system included a downtown subway station interface with the Red Line metro and a short section of subway before reaching the surface of proceed as street trackage and then open ballasted track on a railroad right-of-way.

St. Louis — While this system’s costs were minmimized by predominant use of former railroad right-of-way, a downtown freight rail tunnel was rehabilitated to accommodate the system’s double-track LRT line, with stations; an existing bridge over the Mississippi River was adapted; and significant elevated facilities were installed for access to the metro area’s main airport.

Hopefully this cost data may be helpful to other communities, in providing both a “ballpark” idea of the unit cost of new LRT, and a reality check of any estimated investment cost already rendered of such a new system. ■

Cases where voters okayed rail transit after first rejecting

Rail transit ballot measures are critical events. But if one is rejected, is it a "catastrophic" for the community? Graphic:

Rail transit ballot measures are critical events. But if one is rejected, is it a “catastrophic” setback for the community? Graphic:

Voter rejection of a rail transit project is almost always unfortunate.

But is it catastrophic? Does it signal that the majority in a community will persistently and permanently reject any rail project, or does it represent a more temporary setback, with remaining hope that a better plan, a better presentation to voters, at a better time, could have a chance to win approval?

This issue often arises not only in communities where a rail transit project has unified support from transit advocates, but even in cases where an official plan has faced strong opposition from rail transit supporters. In an effort to mobilize support, proponents of the given project may argue that it may be the community’s “only chance for rail”, that, no matter its deficiencies, a given plan cannot be allowed to fail, because it would be a “disaster”, setting back rail development for decades, perhaps forever.

To evaluate the validity of this argument, and assess the actual delay between the failure of rail ballot measures and the ultimate passage of support for a subsequent rail transit ballot initiative, the LRN Project team examined available cases since 2000 where an initial rejection of rail was followed by a successful later vote. LRN’s approach has examined this issue strictly from the standpoint of attracting voter support — in other words, if the issue of rail transit is re-voted, how long does it take to win approval?

It should be noted that this study has examined the sequence of events only in cities where, after the failure of an initial measure, a new measure for rail transit (often with a somewhat different plan) was offered to voters. In other cases, poorly prepared or presented rail plans were rejected by voters, but rail planning was subsequently dropped (e.g., Spokane, Columbus) or has proceeded without needing a public vote (e.g., San Antonio).

Thus this study has sought to address the question: If rail has previously been rejected by voters, but a new rail measure is subsequently presented for a vote, how long does it take to achieve successful voter approval for rail?

Since 2000, there have been six cases where such re-votes have occurred:

Austin — A plan for a light rail transit (LRT) system was very narrowly defeated in 2000; rail transit was subsequently repackaged as a light railway using diesel-multiple-unit (DMU) rolling stock, and passed in 2004 (now branded as MetroRail). Delay between votes: 4 years.

Kansas City — An officially sponsored LRT plan was defeated in 2001; a different LRT plan initiated by a citizens’ referendum was subsequently approved in 2006. (However, the successful vote was annulled by the city council; implementation of an officially sponsored streetcar project is now underway without a public vote.) Delay between votes: 5 years.

Cincinnati — An LRT plan was rejected in 2002. Rail transit was subsequently repackaged as a streetcar plan that was forced to a public vote, and ultimately was approved in 2009. (A re-vote, forced by opponents’ referendum, was held in 2012, and the streetcar project again passed.) Delay between votes: 7 years.

Tucson — An LRT plan was rejected in 2002; rail transit was subsequently repackaged as a streetcar plan, then submitted for a public vote and approved in 2006. (The new system, branded as Sun Link, is due to open later this year.) Delay between votes: 4 years.

Seattle — A multi-modal transportation proposal, Roads and Transit, including LRT expansion, was defeated in 2007 (with opposition from environmental organizations and other traditional pro-transit groups, dissatisfied with the plan’s heavy highway element). A new package, Sound Transit 2, was prepared, with much heavier transit emphasis, and presented and approved by voters in 2008. Delay between votes: 1 year.

St. Louis — Proposition M, including funding for the region’s MetroLink LRT system, was defeated by voters in 2008. A new package, Prop. A, aided by an improved campaign, and including funding to improve and expand LRT, was subsequently approved in 2010. Delay between votes: 2 years.

From these experiences, it’s plausible to conclude the recent re-votes on rail transit have taken from one to seven years to succeed. This would not seem to suggest that initial loss of a vote results in a “catastrophic” delay of “decades” before a rail transit project can muster approval.

On the contrary, the average delay, on the basis of these cases, is 3.8 years. However, the data seems to suggest a pattern, whereby the delay before a successful rail transit re-vote is less in cities already operating some form of rail transit (Seattle, St. Louis), in contrast to cities where rail would be a totally new addition to the transit mix (Austin, Tucson, Kansas City, Cincinnati). This differential in average delay is illustrated graphically in the chart below:

Left bar: Average years of delay in cities already operating rail transit. Right bar: Average delay in cities with no current rail transit.

Left bar: Average years of delay in cities already operating rail transit. Right bar: Average delay in cities with no current rail transit.

Other than to infer that the loss of a vote does not inevitably represent a “catastrophic” setback for rail transit in a given city, this study with its very small data set does not offer a basis for strong conclusions. However, there is opportunity for plausible speculation:

• Conditions for a more speedy re-vote and approval of a rail transit ballot measure may be more propitious in communities that already have experience with successful rail transit systems.

• The process of re-submitting a rail transit measure to a vote may depend not so much on public attitudes but on the determination of sponsoring officials, their responsiveness to public input, and their willingness to re-craft specific project details to more closely conform to public needs and desires.

How rail public transportation has been a leader in the Analytics and Big Data revolution

Diagram from Digi International illustrates some of the multiple ways that Analytics and Big Data may be involved with rail public transport operation.

Diagram from Digi International illustrates some of the multiple ways that Analytics and Big Data may be involved with rail public transport operation.

In a paper presented this past June (2013) to the annual Rail Conference of the American Public Transportation Association (APTA) in Philadelphia, Light Rail Now Project technical consultant Lyndon Henry (also an independent transportation planning consultant with Urban Rail Today and a blog columnist for Railway Age magazine) emphasized the leading role that rail public transportation has been playing — actually for a number of decades — in the Analytics and Big Data revolution that has been sweeping through both the private and public sector of global economies. (Lyndon is also a blog writer for the All Analytics online forum, sponsored by business analytics provider SAS.)

The paper — titled Analytics and Big Data — Rail Public Transportation is a Leader — not only highlighted a wide variety of applications of the newly emerging capabilities of this technology, but also the long developmental legacy in which rail public transportation has been a pioneer.

So, what are Analytics and Big Data, anyway? This is best explained in the paper’s introduction itself:

Two concepts currently at the leading edge of today’s information technology (IT) revolution are Analytics and Big Data. Analytics is high-technology applied to data processing, complex calculations, and automation; Big Data is the current term referring to significantly large volumes of data, on virtually every facet of human activities and characteristics, that can be rapidly processed via Analytics, yielding a broad spectrum of highly useful results. Recent technological advances have sparked what amounts to a “revolution” in the application of these cognitive and informational tools.

“Apparently without realizing it,” observes the paper, “the public transportation industry, has, for many decades, been at the forefront in utilizing and implementing Analytics and Big Data, from ridership forecasting to transit operations.” As it goes on to explain:

Rail transit systems have been especially involved with these IT concepts, and tend to be especially amenable to the advantages of Analytics and Big Data because they are generally “closed” systems that involve sophisticated processing of large volumes of data. In virtually any American city, on any normal weekday, one is likely to see the results of analytics literally in motion — the operation of transit buses and trains that are essential to maintaining the mobility of the metro area.

The more that public transportation professionals and decisionmakers understand the role of Analytics and Big Data in their industry in perspective, the more effectively they will be able to utilize its promise. Furthermore, it is useful for both the public and the industry to realize how significantly public transportation has been a leading pioneer in the rich and extensive historic development of these tools, the roots of which in some cases extend back to 19th century rail technology.

The paper then details a number of the major general applications of Analytics and Big data in modern rail passenger and rail transit systems:

Travel Demand Modeling — how analytics has actually been used for decades in planning new public transportation services and infrastructure

Train Signal and Control Systems — involving components and technologies such as automatic block signaling (ABS), cab signaling system (CSS), centralized traffic control (CTC), automatic train stop (ATS), automatic train control (ATC), communications-based train control (CBTC), automatic train operation, or ATO, and positive train control (PTC)

Route Planning and Scheduling — involving analytics-based software for tedious tasks such as routing, developing timetables, blocking (developing bus and train schedules),runcutting, and essential component tasks such as rostering

Automatic Vehicle Location (AVL) — this transit operating mechanism deploys analytics to track vehicles in operation and and provide information to passengers via passenger information display (PID) monitors or digital signs in stations, or apps on smartphones

Automated Fare Collection (AFC) — typically relying on ticket vending machine (TVM) devices in stations that can receive cash or process credit card swipes, thus also instantly updating a central database

Automated Passenger Counting (APC) — tallies how many passengers are boarding or deboarding each vehicle, and precisely where this happens, and relays this information continuously to a central database

To illustrate some of these applications, a number of case studies are highlighted from actual operating systems:

Bay Area Rapid Transit (BART) — focusing on agency’s operational analytics providing delay analysis, passenger flow modeling (PFM), system performance analysis, and operational forecasting.

Salt Lake City TRAX — focusing on CTC train tracking and dispatching system, the GPS-based passenger information system, and the AFC system.

Austin – Capital Metro’s MetroRail — focusing on operational analytics involved with the ABS (automatic block signal) system overseen by CTC (central traffic control), the GPS-based AVL and passenger information system, and APC.

Philadelphia – SEPTA Regional Rail — focusing on the system’s signaling-dispatching technology, mainly involving a combination of CTC, ABS, and ATC, plus CSS and PTC via compatibility with Amtrak’s Advanced Civil Speed Enforcement System (ACSES).
In addition, Analytics is being utilized in the form of APC, as well as planning and scheduling, plus a passenger information system utilizing both PIDs with train arrival/departure onfprmation and a smartphone app providing bus and train status information to passengers’ personal devices.

Philadelphia – SEPTA Suburban Trolley Lines — focusing on these lines’ signal systems, currently ABS-based, but with planned conversion to CBTC is being planned.

Seattle – Sound Transit’s Link and Sounder — focusing on these operations’ enhanced AFC system utilizing the new regional, trans-agency ORCA payment card.

Tacoma – Sound Transit’s Tacoma Link Streetcar — focusing on how this very small, simple, extremely bare-bones system integrates its APC system with onboard GPS, and provides on-train passenger announcements triggered by wheel pulses from the cars’ propulsion sensors that gauge distance traveled.

The paper concludes with an overview of some of the most salient current issues and trends in Analytics and Big Data, and their relevance for rail public transportation. Topics include:

Data Mining

Cloud Computing

Sentiment Analysis

Security issues

Privacy Concerns

Predictive Analytics


Check out the full paper for a more detailed discussion of these topics.

A PowerPoint slide show presented at the conference is also available online.

Bus operations as precursors of light rail transit

Seattle — Link LRT trains (left) and BRT buses share the Downtown Seattle Transit Tunnel, originally installed as a busway.

Seattle — Link LRT trains (left) and BRT buses share the Downtown Seattle Transit Tunnel, originally installed as a busway.

by Lyndon Henry

High-quality bus services – often characterized as “Bus Rapid Transit” (BRT) – are frequently portrayed as possible precursors of electric light rail transit (LRT) systems. But can BRT or “BRT-like” bus operations effectively fulfill such a role?

This question was examined in a paper that I and my colleague Dave Dobbs (executive director of the Texas Association for Public Transportation, and publisher of the Light Rail Now website) presented to the Joint International Light Rail Conference co-sponsored by the U.S. Transportation Research Board and the American Public Transportation Association in April 2009 in Los Angeles.

The formal paper, Bus Rapid Transit as a Precursor of Light Rail Transit? is available online, starting at p. 137 of the full conference proceedings:

Our study includes general research as well as an examination of several specific case studies, drawn from both actual operations and planned operations. Our analysis identifies factors that may optimize the capability of of these kinds of bus operations to function more effectively as precursors of LRT systems, but emphasizes that “initial system design, to permit a transition, is critical, and major challenges and drawbacks must be addressed and overcome.”

Our conclusions spell out some details:

This analysis has identified certain factors that may optimize the capability of Bus Rapid Transit to function effectively as a precursor of a light rail transit system. However, it is important for planners to keep in mind that initial system design, to permit a transition, is critical, and major challenges and drawbacks must be addressed and overcome. A major consideration is that the BRT facilities should not represent an obstacle to the subsequent LRT project. As noted, BRT-specific infrastructure (including stations) should ideally be designed to be very low in cost so the sunk cost for BRT is not an impediment to eventual conversion to LRT.

The examples of actual or prospective BRT-to-LRT conversion in both Seattle and Ottawa (and in fact Guadalajara as well) involve some degree of transit service shutdown or disruption on the BRT facility during the conversion process in these types of “high-end”, exclusive facilities. In contrast, a “lower-end” express-bus type of BRT service can probably more readily continue a parallel service on adjacent highway or arterial lanes (if they are available) during the conversion period – although generally without stations and intermediate interchange of transferring passengers (an essential characteristic of LRT which planners should seek in BRT if the BRT service is intended to offer really the same kind of service as LRT).

In addition, the staging and logistics of conversion must be considered, particularly to avoid or minimize disruption of the existing BRT-type service while the LRT installation project is under way. In this regard, alignments in or alongside existing arterials provide at least some opportunity for maintaining a parallel BRT or bus-substitute service; on the other hand, alignments that have appropriated railway ROW for BRT (such as the Ottawa Transitway) make it virtually impossible to maintain a true parallel bus service – thus representing a serious obstacle facing conversion to LRT.

On the whole, the case studies cited suggest that actual experience is still inconclusive as to full cost-effectiveness of some forms of BRT service functioning as precursors to LRT and other type of rail transit. However, several examples approaching implementation in the near future appear to show show promise. As these planned BRT-to-LRT conversions become operational, an updated assessment should be carried out.

For additional information, plus useful graphics, check out the PowerPoint presentation shown at the conference:

Bus Rapid Transit as a Precursor of Light Rail Transit?