Plug-in and Ride: The Promise and Potential Challenges of Electric Buses

The use of electric buses and other zero emission vehicles (ZEVs) holds great promise to help reduce vehicle emissions and promote a clearer, less polluting transportation sector.

Transit bus systems offer a great venue for deploying and testing the latest ZEV technologies. An estimated 40 U.S. transit systems now include electric-power buses as part of their fleet. To date, bus systems in California have been the greatest adopters of electric buses. The Santa Barbara Metropolitan Transit District began using electric buses in 2003 and currently has 14 in operation. Stanford University Transit presently has a fleet of 23 electric buses, which it launched in 2014. Foothill Transit in Northern California started using electric buses in 2010 and now has 30 in use. Foothill Transit has pledged to change all its buses over to electric power by 2030. Foothill Transit estimates that already, its annual electric buses eliminate the same amount of emissions as 2,424 gasoline-powered cars. A number of other California transit agencies have smaller fleets of electric buses.

Two UMTC Research Affiliates recently developed a comprehensive review of past and current electric bus deployments nationally. This research was led by Professor Eleni Christofa in Civil and Environmental Engineering and Professor Krystal Pollitt in Environmental Health Sciences. The review included discussions of the three main types of electric-power buses currently in use, and of different facets and impacts of transit agencies’ change to electric buses, including areas of challenge.

The primary type of electric bus in use today is the battery electric (BE) bus, and more than 20 U.S. transit agencies have incorporated BE buses into their operations, including the Worcester Regional Transit Authority (WRTA) and the Pioneer Valley Transit Authority (PVTA). BE buses contain an onboard electric battery, which provides all their power. These batteries are typically re-charged through plug-in stations; BE buses also capture and then use energy from regenerative braking. BE buses have no direct vehicle emissions, but there may be atmospheric pollutants associated with the generation of electricity used for charging their onboard batteries. One potential challenge with BE buses is the short driving range (30 to 130 miles) before needing to be recharged, and the impact of the need for recharging on route scheduling. These buses will typically be recharged at bus stop charging stations during their routes for quick charges (5 to 15 minutes). Some transit agencies also utilize slower charging stations at a central location such as a bus garage, for when BE buses are out of service. Even with the quick charges, it is important that bus schedules be adjusted to reflect the charging time.

BE buses are more expensive to purchase than traditional diesel-engine buses ($750,000 per bus compared to $435,000 per bus, respectively); however, they have a longer expected lifespan than diesel buses. BE buses also save fuel and maintenance costs. Proterra has stated that overall, the lifecycle costs of BE and diesel buses are similar. The PVTA estimates that each of its BE buses will save the agency $448,000 combined in fuel and maintenance costs. The PVTA also calculated that each of its BE buses will eliminate 244,000 pounds of carbon dioxide emissions compared to their diesel bus counterparts.

The second main type of zero-emissions buses are those powered by hydrogen fuel cell batteries. Fuel cell battery electric (FCBE) buses store hydrogen onboard in storage tanks and the hydrogen is then supplied to the fuel cells to generate electricity to power the vehicles. There are no emissions, as water is the only by-product for FCBEs. There are presently seven U.S. transit agencies operating FCBE buses; the electric bus at the Massachusetts Bay Transportation Authority (MBTA) uses FCBE technology.

With a typical purchase price of $1.2 million, an FCBE bus is much more expensive to purchase than a conventional diesel bus ($435,000) or a compressed natural gas bus ($500,000). FCBE buses also require special training for bus operators on using the technology and special hydrogen storing and fueling facilities; these are typically located at bus depots to allow vehicles to be refueled at day’s end. On the plus side, the fuel economy for FCBE buses has been reported to be double that for compressed natural gas or diesel buses.

The third main type of zero emission buses are fuel cell hybrid (FCH) plug-in buses which use a combination of both onboard batteries and hydrogen fuel cells. To date, only 7 U.S. transit agencies have used FCH buses, mainly in short-term demonstration projects. Transit agencies that have tried FCH buses have consistently reported significant downtime for the buses, due to issues with the batteries, the fuel cell systems, and the hybrid integrator, and to challenges in diagnosing specific problems.

Currently, BE buses seem to hold the most promise for wider deployment and use.

Written by:  Tracy Zafian, UMTC Research Fellow

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Bridgewater State University becomes a “LivingLab”

This summer, Bridgewater State University (BSU) has become a “LivingLab” with the BSU GeoGraphics Lab using campus parking lots to research Small Unmanned Aircraft System Remote Sensing Platforms for transportation.

Lab co-directors and UMTC Research Affiliates Dr. Uma Shama and Mr. Lawrence Harman are using aircraft systems produced by the company DJI to develop image archives of near-empty parking lots on campus with the research goal of using those archives as a baseline for analysis of BSU’s smart parking initiatives.  The aircraft systems (sUAS and software) are low-cost, but they are still able to create high-resolution 2D and 3D web mapping data that emphasizes transportation flows within the LivingLab area.

The research is also working to create Imagery Collection Techniques that comply with the flight rules established in 2016 by the Federal Aviation Administration.  These rules include restrictions on operating unmanned aircraft over people or private property.

Potential applications of the research outside of Bridgewater State, include real-time monitoring and time-series analysis for evaluating mobility investments made by MassDOT, municipalities, and the Massachusetts Bay Transportation Authority.

Partnerships have been a key part of the project’s success.  Collaboration between the BSU Facilities Management and Planning Department, the BSU Police Department, the BSU Aviation Science Department, and the Veterans and Military Affairs Office have been established.  Partnerships have also extended to regional planning and regional transit agencies.

This “LivingLab” initiative builds on a decade of remote sensing research that seeks to apply spatial information technologies to mobility management issues and will help to improve public services locally, regionally, and globally.

Written by: Dr. Uma Shama and Mr. Lawrence Harman, Bridgewater State University; and edited by UMTC staff.

Autonomous Vehicle Research: MassDOT Leads the Way

More U.S. states are considering legislation and regulations for highly automated vehicles (HAVs) testing. Twenty-four states and the District of Columbia have now enacted legislation regarding the testing of highly autonomous vehicles. Only Michigan currently allows the driverless HAVs on public roads; California is considering the same but has not approved it yet.

The federal policy (Federal Automated Vehicles Policy) provides guidance for those developing, testing, and deploying highly automated vehicles. The policy considers current and potential regulatory tools that could be used with these vehicles. The policy also describes the different responsibilities on the federal and state levels, and creates a model for state policy that recommends policy areas for states to consider for automated vehicles.

Figure 1: States with Enacted Legislation for Autonomous Vehicles

IOpicAs of July 27, 2017. Source: National Conference of State Legislatures. http://www.ncsl.org/research/transportation/autonomous-vehicles-self-driving-vehicles-enacted-legislation.aspx

In October 2016, Massachusetts Gov. Charlie Baker signed Executive Order No. 572, To Promote the Testing and Deployment of Highly Automated Driving Technologies (EO 572). EO 572 created a state government working group on autonomous vehicles (AV Working Group). The group’s charge is to “convene and consult with experts on motor vehicle safety and vehicle automation…and [to] work with the Legislature on any proposed legislation necessary to protect the public welfare.” The AV Working Group is led by Katherine Fichter, Massachusetts Department of Transportation (MassDOT) Assistant Secretary for Policy Coordination and Transportation Secretary Stephanie Pollack’s designee to the group. The AV Working Group also includes other MassDOT staff and representatives from the State Police, the Executive Office of Public Safety and Security, Housing and Economic Development, and the State Legislature.

One Center, at the UMass Transportation Center, has recently contracted with UMTC Research Affiliates, at UMass Lowell, to conduct research on the technological developments, regulatory requirements, funding opportunities, and potential benefits of the emerging AV technology to take appropriate actions for the benefit of the citizens of the Commonwealth. The affiliates associated with this research are Chronis Stamatiadis, Nathan Gartner, Yuanchang Xie, and Danjue Chen. This project will provide baseline information pertaining to strategic planning for connected vehicle (CV) technologies. This information will be used by MassDOT to develop a strategic plan for the development and deployment of connected vehicle technology and infrastructure in Massachusetts.

EO 572 authorized MassDOT, with input from the AV Working Group and other technical experts, to develop and issue guidance for testing highly automated vehicles on public roadways in Massachusetts, and includes a process for companies to obtain approval for such testing.

Highly automated vehicle testing on public roadways is under the authority of MassDOT. Presently in Massachusetts, most testing takes place in spaces and courses outside of MassDOT’s jurisdiction, such as universities, private indoor testing facilities, and the former Fort Devens military base.

As described by Boston National Public Radio station WBUR, nuTonomy, a Massachusetts Institute of Technology (MIT) spinoff company, began the first testing of highly automated cars on Boston roads in January 2017. The initial testing area was limited to a 191-acre industrial park in South Boston, the Raymond L. Flynn Marine Park, which has a simple road layout, no traffic signals, and only 3 miles of roadway. At first, testing was approved only for daylight hours and good weather, but then was expanded to nighttime and inclement weather. The company has now logged over 200 miles of automated vehicle driving in the industrial park, with no crashes or incidences. With these results, in April 2017, nuTonomy was granted approval to expand its HAV testing to the Seaport and Fort Point areas. A Boston Globe article discussed this approval and interviewed City of Boston and nuTonomy staff. The Seaport roadways are considerably more complex than the testing roads so far, including more complicated intersections, traffic signals, roadways with multiple lanes, bridges, and a rotary. As before, nuTonomy’s testing in the expanded area initially was for daylight hours and good weather only.

In June 2017, MassDOT granted permission for a second MIT-spinoff company, Optimus Ride, to test highly automated vehicles on Boston roads. As described in a Boston Globe article, Optimus Ride will initially test its vehicles only in the Raymond L. Flynn Marine Park, as nuTonomy did.

During their HAV roadway testing, nuTonomy and Optimus Ride both have a human operator sitting in the driver’s seat, ready to take over control of the vehicle if needed. This is currently standard for most on-road testing of HAVs. Some companies use two human workers, one in the driver seat and one in the front passenger seat, to help sustain vigilance and monitoring of the HAV’s driving and the ability to switch to manual driving mode if ever needed. As described in its road test application to MassDOT, after 200 miles of testing, Optimus Ride may request MassDOT permission to test its vehicles with passengers.

In terms of legislation and regulations for automated vehicles (AVs), in her keynote talk at a recent conference on Autonomous and Connected Vehicles held at Worcester Polytechnic Institute, Ms. Fichter indicated that Gov. Baker and MassDOT have taken the position that it is better not to regulate AVs through legislation. AV and HAV technologies are still evolving, and legislation can be difficult to modify once passed. In the Massachusetts Legislature, there are currently eight bills that have been filed related to AVs. On July 13, 2017, the AV Working Group held a legislative meeting to discuss them and hear more about them from their proponents. The MassRobotics Consortium has posted its notes from the meeting. Most of the bills include guidance for AV safety and for liability in the event of a crash involving an AV, with no liability assigned to the original manufacturer of a vehicle that has been later converted to an AV. Joint bills S. 1945/H. 1829 also request that all AVs be zero emission vehicles (ZEVs), encourage AVs to be for public transit only in areas with dense populations, provide guidance for AV data collection, and propose having a vehicle-miles-traveled (VMT) tax on AVs. The idea of a VMT-based tax raised questions and issues at the meeting, related to such issues as geographic equity, fuel consumption and encouraging efficient vehicles, and collection of vehicle owners’ travel data, as well as the need for additional revenues as more vehicles are converted to AVs and electric vehicles.

Among the other proposed AV legislation, H. 2742 requires that AVs used for the interstate transport of goods or for transporting eight or more people be required to have a human operator present who can intervene if needed. Bills S. 1938 and H. 3422 both focus on making AVs that do not require a human operator available to the public. Bills H. 1822 and H. 1897 each request that MassDOT submit a report to the state House and Senate leaders “recommending additional legislative or regulatory action that may be required for the safe testing and operation of motor vehicles equipped with autonomous technology.” H. 1897 requests such a report by June 2017, while H. 1822 requests it by March 2019.

At the end of the July AV Working Group meeting, Ms. Fichter recommended the next meeting would be in September 2017. At this meeting, people from the AV industry will present and provide their perspectives regarding AVs and HAV regulation, and how AV technologies will come to market.

 

 

 

Written by: Tracy Zafian, UMTC Research Fellow

 

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UMTC Affiliates & MassDOT Assistant Secretary Katherine Fichter Present at WPI Conference on Vehicle Automation

 

In May 2017, Worcester Polytechnic Institute (WPI) held its second annual Connected and Autonomous Vehicles Summer School speaker series, sponsored by the Institute of Electrical and Electronics Engineers Vehicular Technology Society (IEEE VTS). The event included two days of lectures and discussions.

CAV intersection
Photo source: U.S. Department of Transportation
  • Danjue Chen, Professor at UMass-Lowell and UMTC Affiliate, discussed the impacts of connected and automated vehicles (CAVs) on traffic operations and highway traffic flow, and how CAVs can help optimize roadway capacity and traffic control. Professor Chen is the featured researcher in this month’s Innovative Outlook (IO).
  • Hossein Pishro-Nik, Professor at UMass-Amherst and UMTC Affiliate, spoke about Vehicular Ad Hoc Networks (VANETs) for vehicle-to-vehicle and vehicle-roadway infrastructure communications. His talk discussed the relationship between communications and safety in VANETs, how VANETs can be customized for different traffic conditions and individual drivers, and the issues of privacy in VANETs and Internet-connected devices and applications. Professor Pishro-Nik’s research is described in more detail in another post.
  • Jason Rife, Professor at Tufts University, presented information on different GPS-based technologies and applications that can assist with automated vehicles and navigation, even in dense urban areas with limited sky visibility.
  • Bob Sletten, Engineering Manager at Autoliv, a company that develops automotive safety systems for auto manufacturers, spoke about radar technology in automotive applications.
  • Akshay Rajhans, Senior Research Scientist at MathWorks, spoke about model-based design for connected autonomous vehicles. As described in the WPI conference program, “model-based design makes use of computational models of systems under design that are developed, optimized and checked after correctness specifications throughout the design cycle.”
  • Alexander Wyglinski, WPI Professor and organizer of the conference, provided an overview of vehicular communication systems and the fundamental concepts for understanding, designing, and implementing them.

The keynote speaker at the gathering was Katherine Fichter, Assistant Secretary for Policy Coordination at MassDOT. Ms. Fichter discussed the potential future impacts of driverless vehicles under different scenarios, including a Driverless Utopia and a Driverless Nightmare that were described in Driving Towards Driverless Cars, a blog by Lauren Isaac. Under these scenarios, autonomous vehicles are expected to improve roadway safety, increase vehicle miles traveled, and reduce greenhouse gas emissions, but there are other potential impacts that are less certain. For example, will more driverless cars reduce urban sprawl or increase it, and how will the mobility of low-income people be impacted? As Ms. Fichter discussed, there are questions as well about how autonomous vehicles will be regulated and insured. One big challenge is that current regulations are all based on the idea that vehicles have human operators; this will need to change.

Written by Tracy Zafian, UMTC Research Fellow.

Customizing Your Self-Driving Car

In the future, intelligent transportation systems (ITSs) will involve connected vehicles, including driver-assisted vehicles and self-driving cars, as well as on-board mobile devices, sensors, and the software and algorithms that govern the functioning of these devices and their communications. Despite recent improvements, each year tens of thousands of lives are lost and billions of dollars are wasted because of traffic inefficiencies in the United States alone. Improvements in the transportation systems could have an enormous impact on lowering these statistics.

In this research, we aim to establish a new approach in design of safety systems, which is based on the individualization and customization of these systems to specific drivers and their environments. This means that wireless communication protocols, as well as algorithms that communicate to users, can be designed in an intelligent way in order to take advantage of all the statistical data that is available regarding the driver and his/her environment.

To accomplish this objective, we can use the technology to collect driver performance data and subsequently learn driver characteristics and driving strategies. This information, along with data collected from other vehicles and roadside units, can be used to customize the technology to each driver. With this, it is possible to adapt warnings or automatic control strategies to each driver. Meanwhile, vehicle-to-vehicle (V2V) communication can be dynamically tuned to make efficient use of finite bandwidth and guarantee the transmission of information critical to safety.

In this way, we should consider that there is an uncertainty of the message delivery between two specific vehicles, while other vehicles might also transmit simultaneously. Our research shows that by proper adaptation of wireless communication and warning algorithms, we can potentially reduce accident fatalities by a considerable amount.

To understand the benefit of V2V communication, consider a traffic stream where a chain of vehicles moves with same speed. When the first vehicle in the chain brakes, the driver of the following vehicle applies the brake after her perception reaction time (PRT). If no intervehicle communications are employed, vehicle Vi applies the brake after the sum of PRTs up to the driver i. With the communications, this time will change to the communications delay plus PRTs of the driver i. This is shown in Figure 1.

Hossein_Fig1new
Figure 1. Communications delay versus sum of PRTs, illustrating the time before a driver in a chain applies the brake

Some drivers may think that some of the received warning messages are not needed, because the drivers are aware of their own response time empirically and they know that they can react to stimuli fast. These warning messages are false alarms for these drivers. These warning messages may frustrate the drivers with an overly high number of false alarms, causing them to ignore warnings or even disable the system. To address this issue, we propose estimating the PRT of drivers and personalizing warning messages based on individual PRTs. Figure 2 shows that at the same accident probability for each driver, the false alarm rate can be reduced by at least 30% by employing the estimated individual distribution instead of the population distribution. Thus, it is of vital importance to minimize false alarms so that the system sends warnings only when they are needed.

Hossein_Fig2new
Figure 2. False alarm rate versus the probability of accident based on using average response time or individual

Now, we should determine how channel access probabilities of vehicles and vehicular communications can be adapted to drivers’ characteristics. In a network of vehicles, each vehicle transmits with a specific probability in the transmission medium. Large channel access probabilities lead the system to excessive interferences and, consequently, low probability of packets being successfully received (success probability), while very small values reduce the success probabilities since the probability of the favorite transmission is low itself. Therefore, there is an optimal value, given both the physical data obtained by vehicular networks and the communications protocol requirements, which results in lower collision probability of vehicles. We can find the expression of packet success probability in a network of vehicles based on channel access probability of vehicle.

We then use a recursive algorithm to tune the transmission probability of each vehicle based on the individual characteristics of drivers. The PRT of the driver, traffic conditions, and communications delay are three factors that play roles in assigning channel access probabilities to vehicles. In simple terms, we categorize the drivers into safe and unsafe drivers based on perception-reaction time. The unsafe vehicles are the ones whose drivers have long perception-reaction time and low distance to the vehicle in front. In other words, unsafe vehicles have higher collision probability. Then we assign different channel access probabilities to unsafe and safe vehicles respectively.

Figure 3(a) illustrates the collision probabilities when channel access probabilities are assumed to be equal for all vehicles. Figure 3(b) shows the scenario in which different channel access probabilities are assigned to unsafe and safe vehicles. The minimum collision probability in the second scenario improves by 25%.

Hossein_Fig3a
Figure 3(a). Collision probabilities when channel access probabilities are equal for all vehicles
Hossein_Fig3b
Figure 3(b). Different channel access probabilities are assigned to unsafe and safe vehicles.

Our simulation results confirm that unsafe vehicles need to inform other vehicles of their perilous situation more frequently than do safer vehicles. In other words, with higher channel access probability for unsafe vehicles, we can achieve lower collision probabilities.

Written by Hossein Pishro-Nik, UMTC Research Affiliate and Associate Professor in the Department of Electrical and Computer Engineering (ECE) at UMass-Amherst. This research was supported by the National Science Foundation under Grant CCF– 0844725 (PI: Hossein Pishro-Nik). It is a joint work with ECE PhD students Mohammad Nekoui, Ali Rakhshan, and Mohammad Kohsravi, and Professor Daiheng Ni from the UMass-Amherst Department of Civil and Environmental Engineering. For more information and access to published papers, please visit http://www.ecs.umass.edu/ece/pishro/publications.html.

GM Rolling Out AV Fleet

General Motors Company (GM) announced in mid-June that it completed production of 130 self-driving Chevrolet Bolt electric vehicles for testing automated vehicle (AV) technologies on-road. These highly automated vehicles (HAVs) join GM’s more than 50 Chevrolet Bolts with AV technologies already operating on public roads in San Francisco, Detroit, and Scottsdale, Arizona. In April 2017, Spectrum, the flagship magazine for the Institute of Electrical and Electronics Engineers (IEEE), reported on GM plans to have as many as 300 more self-driving vehicles on-road, presumably including the recently completed 130 vehicles. According to Spectrum, GM would then have the largest HAV fleet on-road not only in the United States, but worldwide. Google-based Waymo has the second-largest AV fleet in the United States, with an estimated 160 vehicles on-road.

GM CEO & Chairman Mary Barra with a new Chevrolet Bolt AV (Photo by Paul Sancya, Associated Press)

In GM’s announcement regarding the 130 new self-driving Bolts, GM Chairman and CEO Mary Barra is quoted: “This production milestone brings us one step closer to making our vision of personal mobility a reality …. Expansion of our real-world test fleet will help ensure that our self-driving vehicles meet the same strict standards for safety and quality that we build into all of our vehicles.” CEO Barra has also said that “no other company today has the unique and necessary combination of technology, engineering and manufacturing ability to build autonomous vehicles at scale.”

The new self-driving version of the Chevrolet Bolt is the second generation of GM’s AVs and is capable of handling almost any roadway situation without human driver intervention. The new Bolts are equipped with the latest technologies in cameras, radar (LiDAR), sensors, and related hardware. “There are even a couple of cameras that are dedicated just to seeing traffic lights to make sure you don’t run red lights,” said Kyle Vogt, CEO of Cruise Automation, a self-driving software company that GM acquired in 2016. The GM HAVs always have an employee in the driver’s seat for safety reasons, just in case any intervention is needed. Almost all states with HAV regulations also have the requirement that a human operator be present.

In 2016, GM also partnered with and invested $500 million in ride-sharing company Lyft. In a recent Forbes article, Cruise CEO Vogt wouldn’t confirm a Reuters report that “thousands” of self-driving Chevrolet Bolt hatchbacks will go into service for ride-hailing company Lyft in 2018, but said it wouldn’t be surprising. “We’ve had a plan in place for a while and it’s going according to schedule. From what I can tell it’s much faster and going to happen much sooner than most people in the industry think,” Vogt said. “We’re planning to deploy in a rideshare environment, and very quickly.”

Written by Tracy Zafian, UMTC Research Fellow. 

YouTube Research Spotlight: Research to Improve At-Grade Rail Crossing Safety

The UMTC Research Section Launches a Research Spotlight YouTube Channel. We are showcasing research currently being conducted on “At-Grade Rail Crossing Safety” by Radhameris Gomez.  Ms. Gomez is a PhD candidate in the UMass Transportation Engineering Program at the University of Massachusetts, Amherst. View the overview video (3 minutes) or the extended video (10 minutes) to find out how she became interested in studying transportation engineering.

TrailCrashes at roadway-railroad intersections happen far too often. Federal Railroad Administration data show that 2,025 such crashes occurred in the United States in 2016, resulting in 265 fatalities and 798 injuries. There have been a number of roadway-rail intersection crashes recently. For example, in Florida, an Amtrak train collision with a car left one person dead; in Arkansas, one person was killed and another injured when their car crossed into a train’s path; and in North Carolina, a train crashed into a car that stopped on the railroad tracks when the safety arms came down, and the car driver was killed. Earlier in March, a freight train collided with a charter bus in Mississippi that had become stuck on a rail crossing with low clearance on the crest of a slope. Four people were killed and others injured; it was the 161st crash since 1976 at that crossing. After a March snowstorm, a local DPW worker in Longmeadow, Massachusetts, died when his snowplow backed onto railroad tracks when a train was coming. At that intersection, there are no gate arms or traffic signals to help warn drivers when a train would be coming; there had been five other crashes and four other deaths at that location since the 1970s.

Previous studies have examined primary contributing factors for grade-crossing train-car crashes and how these crashes can be prevented. Jeff Caird and colleagues at the University of Calgary analyzed over 300 grade-crossing crashes in Canada (2002). They estimated that adding flashing lights to a rail crossing without them has the potential to reduce crashes by over 60 percent, as compared to crossbucks alone. Michael Lenné and colleagues at Monash University in Australia conducted a driving simulator study (2010) on driving behavior at three different types of at-grade rail crossings: stop-controlled, with flashing lights, and with a traffic signal. The researchers found that participants slowed their vehicles the most when approaching rail crossings with flashing lights.

By: Tracy Zafian, UMTC Research Fellow

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Pre-signals for Transit Priority

Transit preferential treatments can reduce transit delay and therefore improve the efficiency and reliability of transit systems. Examples include dedicated bus lanes, queue jump lanes, and transit signal priority. However, these treatments are not always feasible due to lack of funding or space. In addition, they can often have detrimental impacts on other users of the system. Sustainability goals that are set by a lot of planning and transit agencies demand solutions that more efficiently utilize existing infrastructure and capacity while providing priority to transit vehicles.

Pre-signals allow for provision of priority to buses traveling on dedicated bus lanes by taking advantage of existing infrastructure and utilizing intersection capacity more efficiently. Pre-signals are additional signals placed upstream of signalized intersections to facilitate provision of some level of priority to buses, as well as other modes, by allowing them to bypass standing queues of cars. Typically, operating pre-signals require the existence of at least two lanes in the direction of travel.

However, recent work has suggested that pre-signals can aid in the temporary utilization of contra-flow lanes for transit priority provision for single lane approaches [1]. In particular, pre-signals are used upstream of the main intersection signals to allow the bus to jump the car queues and be at the front of the queue at the main signal. Pre-signals are used in combination with dedicated bus lanes when there is a need to end the bus lane in advance to allow cars to discharge from the intersection using all lanes. For example, as seen in Figure 1 the dedicated bus lane ends at some distance upstream of the intersection to allow cars to use all three lanes while discharging from the intersection.

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Figure 1. Pre-signal at a three-lane approach with a dedicated bus lane.

The pre-signal works as a regular signal and is coordinated with the main signal to utilize maximum capacity. While the main signal is red, cars receive a red light at the pre-signal and are queued upstream of it. This ensures that a bus arriving during the red period can move to the stop line at the main signal and discharge immediately when the main signal turns green. Cars receive a green pre-signal such that no gaps are created in the traffic stream, and no green time at the main signal is lost when buses are not present. Regardless of the main signal’s phase, a bus approaching the intersection will trigger the pre-signal to turn red for cars, allowing the bus to move to the main signal without encountering conflicting maneuvers from cars.

An example of real-world pre-signal operations can be seen in this video. The video presents the operation of a pre-signal along Langstrasse in Zurich, Switzerland. A dedicated bus lane and one lane for cars exist upstream of the intersection but merge into a single mixed-use lane just upstream of the signalized intersection. A pre-signal at the location of the merge provides priority to buses when approaching the main signal. The pre-signal turns red when the bus is detected approaching the intersection. As a result, the bus travelling on the bus lane can bypass the queue of cars and enter the mixed-use lane at the intersection before the cars arrive. As soon as the bus bypasses the standing queue of cars, the pre-signal turns green again so that cars can proceed through the intersection after the bus.

The concept of pre-signals was first introduced to address lost time due to acceleration and perception/reaction time at the onset of green at signalized intersections and the first pre-signals were installed in Dusseldorf, Germany in 1954 [2]. This first study found that if there are only cars in a traffic stream, the equivalent of approximately 4 seconds of additional green time can be gained at intersections with the use of this type of pre-signal. More recent work has explored the use of pre-signals to increase intersection capacity by resolving various types of vehicular conflicts (e.g., between left and through moving vehicles that are either conflicting or compete for green time at the main signal) that would otherwise occur at the signalized intersection downstream [3,4]. A theoretical analysis of pre-signals for transit priority was first presented by Wu and Hounsell [5]. However, their proposed implementation included a constant pre-signal operation regardless of the arrival of a bus. To the best of our knowledge, real-world implementations of pre-signals are limited. A few locations are known in London, operating in a fashion similar to the one described in [5] and one location has been noted in Zurich, Switzerland.

We are currently working on identifying domains of application for implementation of individual transit preferential treatments or combinations of those for a variety of operating conditions for traffic and transit. Click here for a relevant presentation. 

By: Eleni Christofa, Ph.D., Assistant Professor, UMass Amherst and S. Ilgin Guler, Ph.D., Assistant Professor, The Pennsylvania State University 

[1] Guler, S.I., Gayah, V.V. and Menendez, M., 2016. Bus priority at signalized intersections with single-lane approaches: A novel pre-signal strategy. Transportation Research Part C: Emerging Technologies63, pp.51-70.

[2] Von Stein, W., 1961. Traffic flow with pre-signals and the signal funnel. Theory of Traffic Flow, Elsevier, Amsterdam.

[3] Xuan, Y., Gayah, V., Cassidy, M. and Daganzo, C., 2012. Presignal Used to Increase Bus-and Car-Carrying Capacity at Intersections: Theory and Experiment. Transportation Research Record: Journal of the Transportation Research Board, (2315), pp.191-196.

[4] Xuan, Y., Daganzo, C.F. and Cassidy, M.J., 2011. Increasing the capacity of signalized intersections with separate left turn phases. Transportation Research Part B: Methodological45(5), pp.769-781.

[5] Wu, J. and Hounsell, N., 1998. Bus priority using pre-signals. Transportation Research Part A: Policy and Practice32(8), pp.563-583.

 

T-Force Toolkit : Increasing Truck and Bus Traffic Enforcement

For a variety of reasons, routine traffic stops with large trucks and buses occur significantly less than traffic stops with passenger vehicles. Considering the detrimental effects of these crashes, it is critical that we incorporate truck/bus traffic enforcement into existing highway safety activities.

With this growing issue in mind, the University of Massachusetts Traffic Safety Research Program (UMassSafe) developed T-Force, Truck and Bus Traffic Enforcement Toolkit, providing a free one stop shopping tool for resources geared toward traffic patrol officers. T-Force is a three-part program with a goal of increasing the enforcement of moving violations such as speeding and lane violations. Different than programs aiming to inform specialized Motor Carrier Safety Assistance Program (MCSAP) officers, this information is intended for a wider audience, particularly officers conducting regular traffic enforcement.

The T-Force Toolkit is comprised of three main sections; including Fast Facts, Instructors Portal and Web Resources.

  • Fast Facts: This section of the Toolkit offers detailed information regarding the importance of traffic stops with trucks/buses, strategies for maintaining officer safety, how truck/bus traffic stops are different than those with passenger cars, the process of conducting an effective traffic stop and the details involved in CDL. Users can move quickly through this interactive tool, accessing only the information they need.
  • Instructors Portal: This section provides access to all of the materials needed to conduct the Safe and Effective Traffic Stops: Truck and Bus Traffic Enforcement training. This training, developed by UMassSafe, is currently being taught in several states across the country for local and state traffic patrol officers. Instructors can access all course materials on the website, including a guide for both instructors and participants as well as the PowerPoint presentation.
  • Web Resources: The web resources section provides access to an online library of videos, a discussion board to ask and answer questions, and links to other trainings and online resources.

For additional information  www.tforcetoolkit.com.

UMassSafe is a multidisciplinary traffic safety research group housed in the UMass Transportation Center at the University of Massachusetts. With the unique ability to examine highway safety from a variety of perspectives, UMassSafe provide tools and information in a format that is practical for a wide range of users from law enforcement personnel to statisticians at federal agencies. Working on issues related to commercial motor vehicle safety for over 15 years, UMassSafe has developed data query tools, crash corridor maps, and police training as well as conducted extensive crash data analysis and data quality improvement projects.

By: Robin Riessman and Jennifer Gazzillo, UMassSafe

UMass Researchers Crowdsource Data to Provide Travel Information

Dr. Lance Fiondella gave a talk on “Software Tools to Support Transportation Network Performance and Vulnerability Analysis.”  He highlighted his recent research, working closely with Venkateswaran Shekar, a PhD student, on developing a Smartphone Application that will be able to capture individual geographical coordinates to better understand individual travel behavior. The crowdsourced coordinates are uploaded every 3 seconds which allows the researchers to capture the travel path and time, and then calculate speed of an individual walking, biking or driving. There is also a feature that allows voluntary input of demographic data which will allow for more sophisticated data analysis on travel patterns across key demographics. Researchers are also looking into developing additional features such as allowing the user to call for help and the App will provide geographical coordinates.

Dr. Fiondella is an Assistant Professor at the University of Massachusetts Dartmouth in the Electrical and Computer Science Department. Check out the presentation here. Learn more about Dr. Fiondella here.

By: Melissa Paciulli