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Paper

Analyzing the Effect of Autonomous Ridehailing on Transit Ridership: Competitor or Desirable First-/Last-Mile Connection?

 
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Publication: Transportation Research Record
Volume: 2675 (11)
Pages: 1154-1167
Publication Date: 2021
Summary:

Ridehailing services (e.g., Uber or Lyft) may serve as a substitute or a complement—or some combination thereof—to transit. Automation as an emerging technology is expected to further complicate the current complex relationship between transit and ridehailing. This paper aims to explore how US commuters’ stated willingness to ride transit is influenced by the price of ridehailing services and whether the service is provided by an autonomous vehicle. To that end, a stated preference survey was launched around the US to ask 1,500 commuters how they would choose their commute mode from among choices including their current mode and other conventional modes as well as asking them to choose between their current mode and an autonomous mode. Using a joint stated and revealed preference dataset, a mixed logit model was developed and analyzed.

Authors: Dr. Andisheh Ranjbari, Moein Khaloei, Ken Laberteaux, Don MacKenzie
Recommended Citation:
Khaloei, M., Ranjbari, A., Laberteaux, K., & MacKenzie, D. (2021). Analyzing the Effect of Autonomous Ridehailing on Transit Ridership: Competitor or Desirable First-/Last-Mile Connection? Transportation Research Record, 2675(11), 1154–1167. https://doi.org/10.1177/03611981211025278

Empirical Analysis of Commercial Vehicle Dwell Times Around Freight-Attracting Urban Buildings in Downtown Seattle

 
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Publication: Transportation Research Part A: Policy and Practice
Volume: 147
Pages: 320-338
Publication Date: 2021
Summary:

This study aims to identify factors correlated with dwell time for commercial vehicles (the time that delivery workers spend performing out-of-vehicle activities while parked). While restricting vehicle dwell time is widely used to manage commercial vehicle parking behavior, there is insufficient data to help assess the effectiveness of these restrictions, which makes it difficult for policymakers to account for the complexity of commercial vehicle parking behavior.

This is accomplished by using generalized linear models with data collected from five buildings that are known to include commercial vehicle activities in the downtown area of Seattle, Washington, USA. Our models showed that dwell times for buildings with concierge services tended to be shorter. Deliveries of documents also tended to have shorter dwell times than oversized supplies deliveries. Passenger vehicle deliveries had shorter dwell times than deliveries made with vehicles with roll-up doors or swing doors (e.g., vans and trucks). When there were deliveries made to multiple locations within a building, the dwell times were significantly longer than dwell times made to one location in a building. The findings from the presented models demonstrate the potential for improving future parking policies for commercial vehicles by considering data collected from different building types, delivered goods, and vehicle types.

Authors: Haena KimDr. Anne Goodchild, Linda Ng Boyle
Recommended Citation:
Kim, H., Goodchild, A., & Boyle, L. N. (2021). Empirical analysis of commercial vehicle dwell times around freight-attracting urban buildings in downtown Seattle. Transportation Research Part A: Policy and Practice, 147, 320–338. https://doi.org/10.1016/j.tra.2021.02.019
Paper

Bowtie Analysis without Expert Acquisition for Safety Effect Assessments of Cooperative Intelligent Transport Systems

 
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Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Publication Date: 2018
Summary:
Estimating the safety effects of emerging or future technology based on expert acquisitions is challenging because the accumulated judgment is at risk of being biased and imprecise. Therefore, this semiquantitative study proposes and demonstrates an upgraded bowtie analysis for safety effect assessments that can be performed without the need for expert acquisition. While bowtie analysis is commonly used in, for example, process engineering, it is novel in road traffic safety. Four crash case studies are completed using bowtie analysis, letting the input parameters sequentially vary over the entire range of possible expert opinions. The results suggest that only proactive safety measures estimated to decrease the probability of specific crash risk factors to at least “very improbable” can perceptibly decrease crash probability. Further, the success probability of a reactive measure must be at least “moderately probable” to reduce the probability of a serious or fatal crash by half or more. This upgraded bowtie approach allows the identification of (1) the sensitivity of the probability of a crash and its consequences to expert judgment used in the bowtie model and (2) the necessary effectiveness of a chosen safety measure allowing adequate changes in the probability of a crash and its consequences.

 

 

Authors: Dr. Ed McCormack, Ute Christine Ehlers; Eirin Olaussen Ryengm Faisal Khan, and Sören Ehlers
Recommended Citation:
Ehlers, U. C., Ryeng, E. O., McCormack, E., Khan, F., & Ehlers, S. (2018). Bowtie Analysis without Expert Acquisition for Safety Effect Assessments of Cooperative Intelligent Transport Systems. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 4(4), 04018036.

Seattle Center City Alley Infrastructure Inventory and Occupancy Study 2018 (Task Order 4)

The Urban Freight Lab conducted an alley inventory and truck load/unload occupancy study for the City of Seattle. Researchers collected data identifying the locations and infrastructure characteristics of alleys within Seattle’s One Center City planning area, which includes the downtown, uptown, South Lake Union, Capitol Hill, and First Hill urban centers. The resulting alley database includes GIS coordinates for both ends of each alley, geometric and traffic attributes, and photos. Researchers also observed all truck load/unload activity in selected alleys to determine minutes vacant and minutes occupied by trucks, vans, passenger vehicles, and cargo bikes. The researchers then developed alley management recommendations to promote safe, sustainable, and efficient goods delivery and pick-up.

Key Findings

The first key finding of this study is that more than 90% of Center City alleys are only one-lane wide. This surprising fact creates an upper limit on alley parking capacity, as each alley can functionally hold only one or two vehicles at a time. Because there is no room to pass by, when a truck, van, or car parks it blocks all other vehicles from using the alley. When commercial vehicle drivers see that an alley is blocked they will not enter it, as their only way out would be to back up into street traffic. Seattle Municipal code prohibits this, as well as backing up into an alley, for safety reasons.

When informed by the second key finding‚ 68% of vehicles in the alley occupancy study parked there for 15 minutes or less‚ it is clear that moving vehicles through alleys in short time increments is the only reasonable path to increase productivity. As one parked vehicle operationally blocks the entire alley, the goal of new alley policies and strategies should be to reduce the amount of time alleys are blocked to additional users.

The study surfaces four additional key findings:

  1. 87% of all vehicles in the 7 alleys studied parked for 30 minutes or less. Given the imperative to move alley traffic quickly, vehicles that need more parking time must be moved out of the alleys and onto the curb where they don’t block others.
  2. 15% of alleys’ pavement condition is so poor that delivery workers can’t pass through with loaded hand carts. Although trucks can drive over fairly uneven pavement without difficulty, it is not the case for delivery people walking with fully loaded handcarts. The alley pavement rating was done with a qualitative visual inspection to identify obvious problems; more detailed measurements would be needed to fully assess conditions.
  3. 73% of Center City area alleys contain entrances to passenger parking facilities. Placing garage entrances in alleys has been a city policy goal for years. But it increases the frequency of cars in alleys and adds demands on alley use. Understanding why cars are queuing for passenger garages located off alleys, and providing incentives and disincentives to reduce that, would help make alleys more productive.
  4. Alleys are vacant about half of the time during the business day. While at first blush this suggests ample capacity, the fact that an alley can only hold one-to-two parked trucks at a time means alleys are limited operationally and therefore are not a viable alternative to replace the use of curb CVLZs on city streets.

These findings indicate that, due to the fixed alley width constraint, load/unload space inside Seattle’s existing Center City area alleys is insufficient to meet additional future demand.

Paper

Measurement and Classification of Transit Delays Using GTFS-RT Data

Publication: Public Transport
Volume: 14
Pages: 263-285
Publication Date: 2022
Summary:

This paper presents a method for extracting transit performance metrics from a General Transit Feed Specification’s Real-Time (GTFS-RT) component and aggregating them to roadway segments. A framework is then used to analyze this data in terms of consistent, predictable delays (systematic delays) and random variation on a segment-by-segment basis (stochastic delays). All methods and datasets used are generalizable to transit systems which report vehicle locations in terms of GTFS-RT parameters. This provides a network-wide screening tool that can be used to determine locations where reactive treatments (e.g., schedule padding) or proactive infrastructural changes (e.g., bus-only lanes, transit signal priority) may be effective at improving efficiency and reliability. To demonstrate this framework, a case study is performed regarding one year of GTFS-RT data retrieved from the King County Metro bus network in Seattle, Washington. Stochastic and systematic delays were calculated and assigned to segments in the network, providing insight to spatial trends in reliability and efficiency. Findings for the study network suggest that high-pace segments create an opportunity for large, stochastic speedups, while the network as a whole may carry excessive schedule padding. In addition to the static analysis discussed in this paper, an online interactive visualization tool was developed to display ongoing performance measures in the case study region. All code is open-source to encourage additional generalizable work on the GTFS-RT standard.

Authors: Dr. Andisheh Ranjbari, Zack Aemmer, Don MacKenzie
Recommended Citation:
Aemmer, Z., Ranjbari, A. & MacKenzie, D. Measurement and classification of transit delays using GTFS-RT data. Public Transp 14, 263–285 (2022). https://doi.org/10.1007/s12469-022-00291-7.
Paper

Network Design with Elastic Demand and Dynamic Passenger Assignment to Assess the Performance of Transit Services

 
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Publication: Journal of Transportation Engineering, Part A: Systems
Volume: 146:05:00
Publication Date: 2020
Summary:

This study proposes a solution framework for operational analysis and financial assessment of transit services that considers the passenger behavior and the elasticity of transit demand to service characteristics. The proposed solution framework integrates a dynamic transit passenger assignment model (Fast-Trips) with a mode choice model and a service design module, and iterates these methods until an equilibrium between fares and frequencies is reached. The solution framework was implemented for a newly conceived intercity transit service in Arizona, and the system performance was studied for multiple fare policy and frequency design scenarios. The results showed that the scenarios with designed-oriented frequencies had lower ratios of revenue to operating cost (R/C) compared with those in which frequencies were set based on the passenger path-choice behaviors and route usage, which emphasizes the importance of considering elastic transit demand in network and service designs. The sensitivity analysis also indicated that there are multiple ways to achieve a certain R/C ratio, and therefore it is the other objectives and the operator’s priorities that define the final design and service characteristics.

Authors: Dr. Andisheh Ranjbari, Mark Hickman, Yi-Chang Chiu
Recommended Citation:
Ranjbari, A., Hickman, M., & Chiu, Y. C. (2020). Network Design with Elastic Demand and Dynamic Passenger Assignment to Assess the Performance of Transit Services. Journal of Transportation Engineering, Part A: Systems, 146(5), 04020030. https://doi.org/10.1061/jtepbs.0000326.
Report

Curbing Conflicts: Curb Allocation Change Project Report

 
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Publication Date: 2019
Summary:

Like many congested cities, Seattle is grappling with how best to manage the increasing use of ride-hailing services by Transportation Network Companies (TNCs) like Uber and Lyft. According to a 2018 Seattle Times analysis, TNC ridership in the Seattle region has grown to more than five times the level it was in the beginning of 2015, providing, on average, more than 91,000 rides a day in 2018. And the newspaper reports Uber and Lyft trips are heavily concentrated in the city’s densest neighborhoods, where nearly 40,000 rides a day start in ZIP codes covering downtown, Belltown, Capitol Hill and South Lake Union.

This University of Washington (UW) study focuses on a strategy to manage TNC driver stops when picking up and dropping off passengers to improve traffic flow in the South Lake Union (SLU) area. SLU is the site of the main campus for Amazon, the online retail company. The site is known to generate a large number of TNC trips, and Amazon reports high rates of ride-hailing use for employee commutes. This study also found that vehicle picking-up/dropping-off passengers make up a significant share of total vehicle activity in SLU. The center city neighborhood is characterized by multiple construction sites, slow speed limits (25 mph), and heavy vehicle and pedestrian traffic.

Broad concerns about congestion, safety, and effective curb use led to this study, conducted by researchers at the UW’s Urban Freight Lab and Sustainable Transportation Lab. Amazon specifically was concerned about scarcity of curb space where TNC drivers could legally and readily stop to pick up and drop off passengers. Without dedicated load/unload curb space, TNC vehicles stop and wait at paid parking spots, other unauthorized curb spots, or in the travel lane itself, potentially blocking or slowing traffic. To try to mitigate the impacts of passenger pick-up/drop-off activity on traffic, the city proposed a strategy of increasing passenger loading zone (PLZ) spaces while Uber and Lyft implemented a geofence, which directs their drivers and passengers to designated pick-up and drop-off locations on a block. (Normally, drivers pick up or drop off passengers at any address a rider requests via the ride-hailing app.)

By providing ample designated pick-up and drop-off spots along the curb, the thinking goes, TNC drivers would reduce the frequency with which they stop in the travel lane to pick up or drop off passengers and the time they stay stopped there. By these measures, this study’s findings show the approach was successful. But it is important to note that the strategy is not a silver bullet for solving traffic congestion—nor is it designed as such. It is also important to note that any initiative to manage use of curbs and roads (by TNCs or others) is part of a city’s broader transportation policy framework and goals.

For this study, researchers analyzed an array of data on street and curb activity along three block-faces on Boren Ave N in December 2018 and January 2019. At a minimum, data were collected during the morning and afternoon peak travel times (with some collected 24 hours a day). The research team collected data using video and sensor technology as well as in-person observation. Researchers also surveyed TNC passengers for demographic, trip-related and satisfaction data. The five Amazon buildings in the area studied house roughly 8,650 employees. Researchers collected data in three stages. Phase 1, the study baseline, was before PLZs were added and geofencing started. Phase 2 was after the new PLZs were added, expanding total PLZ curb length from 20 feet (easily filled by one to two vehicles) to 274 feet. Phase 3 was after geofencing was added to the expanded PLZs. The added PLZ spaces were open to any passenger vehicle—not just TNC vehicles—weekdays from 7am to 10am and 2pm to 7pm. (Permitted food trucks were authorized from 10am to 2pm.)

Note that while other cities can learn from this analysis, the findings apply to streets with comparable traffic speed, mix of roadway users, and street design.

The study’s main findings include:

  • A significant percentage of vehicles performing a pick-up/drop-off stop in the travel lane. Those in-lane stops appear connected to the lack of available designated curb space: Adding PLZs and geofencing increased driver compliance in stopping at the curb versus stopping in the travel lane to load and unload passengers. But it was not lack of curb space alone that influenced driver activity: Between 7 percent and 10 percent of drivers still stopped in the travel lane even when PLZs were empty. After adding PLZs and geofencing, in-lane stops fell from 20 percent to 14 percent for pick-ups and from 16 percent to 15 percent for drop-offs.
  • Adding PLZs and geofencing reduced the average amount of time drivers stopped to load and unload passengers. For example, 90 percent of drop-offs took less than 1 minute 12 seconds, 42 seconds faster than the average with the added PLZs alone.
  • While curb occupancy increased after adding PLZs and geofencing, occupancy results show the current allocation of PLZ spaces is more than what is needed to meet observed demand: Average PLZ occupancy remained under 20 percent after PLZ expansion, even during peak commute hours.
  • Vehicles picking-up/dropping-off passengers account for a significant share of total traffic volume in the study area: during peak hours the observed average percentage of vehicles performing a pick-up/drop-off with respect to the total traffic volume was 29 percent (in Phase 1), 32 percent (in Phase 2) and 39 percent (in Phase 3).
  • High volumes of pedestrians (400-500 per hour on average) cross the street at points where there was no crosswalk. Passengers picked-up/dropped-off constituted a fraction (five to seven percent) of those pedestrians, but high rates of passengers (30 to 40 percent) cross the street at non-crosswalk locations.
  • Adding PLZs and geofencing did not have a significant impact on traffic safety. Researchers found no significant change in the number of observed conflicts from baseline to the addition of PLZs and geofencing. Conflicts are situations where a vehicle, bike, or pedestrian is interrupted, forced to alter their path, or engaged in a near-miss situation. Conflicts include vehicles passing in the oncoming traffic lane. • Adding PLZs and geofencing also did not produce a significant impact on roadway travel speed.
  • Of the 116 TNC passengers surveyed in the study area:
    • Roughly 40 percent to 50 percent said their trip was work related. More than half said they used ride-hailing service at least once a week and 70 percent or more used TNC alone (versus in combination with other transportation options) to get from their origin to their destination.
    • Most responded positively to the added PLZs and geofence: 79 percent rated their pick-up satisfactory and 100 percent rated their drop-off satisfactory as compared to 72 percent and 89 percent in the baseline.
    • Nearly half said they would have taken transit and one-third would have walked if ride-hailing was not available.
    • 40 percent requested a shared TNC vehicle in Phase 1 and 47 percent in Phase 3.

The study suggests that while vehicles picking-up/dropping-off passengers account for a significant share of traffic volume in SLU, they are not the primary cause of congestion. Myriad factors impact neighborhood congestion, including high vehicle volume overall and bottlenecks moving out of the neighborhood onto regional arterials. As researchers observed in the afternoon peak, these bottlenecks cause spillbacks onto local streets. Amazon garages exit vehicles onto streets that then feed into these clogged arterials.

Regarding traffic safety in SLU, this study was not designed to assess whether TNC driver behavior on average is safer or less safe than that of other vehicles. It is important to understand the safety and speed findings in the context of the SLU traffic environment. Drivers tend to drive at relatively slow speeds, navigating around high pedestrian and jaywalking volumes, and seem relatively comfortable stopping in the middle of the street for short periods of time. Due to the nature of area traffic, this seems to have relatively little impact on other drivers. Drivers appear to anticipate both this behavior and the high volumes of vehicles moving onto/off the curb and into/out of driveways and alleys.

Whether the strategy this study analyzed is recommended depends on a city’s transportation goals and approach. The researchers found the increased PLZ allocation and geofencing strategy worked in that it improved driver compliance, reduced dwell times, and boosted TNC user satisfaction. However, this may encourage commuters to use TNC. The passenger survey clearly shows that TNC service is attracting passengers who would have otherwise walked or used transit. While in the short term the increased PLZs and geofencing had a positive effect on traffic, if this induces TNC demand, there could be larger, more negative long-term consequences. If the end goal is to reduce traffic congestion, measures to reduce—rather than encourage—TNC and passenger car use as the predominant mode of commuting will yield the most substantial benefits.


In the news:

Geekwire: As Uber and Lyft pick-ups and drop-offs clog traffic, new study calls load zones a move in right direction

The Seattle Times: Seattle Uber and Lyft drivers often stop in the street to pick up or drop off riders. Here’s a way to reduce that.

Recommended Citation:
Goodchild, Anne. Giacomo dalla Chiara. Jose Luis Machado. Andisheh Ranjbari. (2019) Curb Allocation Change Project.

Developing Design Guidelines for Commercial Vehicle Envelopes on Urban Streets

Commercial vehicles using loading zones are not typically provided with an envelope, or a space allocation adjacent to the vehicle for loading and unloading activities. While completing loading and unloading activities, drivers are required to walk around the vehicle, extend ramps and handling equipment, and maneuver goods; these activities require space around the vehicle. The unique needs of a delivery truck are not acknowledged by or incorporated in current design practices.

Due to lack of a truck envelope, drivers of commercial vehicles are observed using pedestrian pathways and bicycling infrastructure for unloading activities and the transport of goods by hand. These actions put themselves, and other road users in direct conflict and potentially in harm’s way. The purpose of this research is to improve our understanding of the interactions between heavy vehicles and other users in an urban environment, in particular, in cases where commercial vehicle activity disrupts the activity of pedestrians and bicyclists. The research approach includes both the observation of current practice and evaluation of infrastructure and simulation of roadway user behavior. This information will support better roadway and load zone design guidelines, which will allow our urban street system to operate more efficiently, safely, and reliably for all users.

Article

Physics-Informed Machine Learning of Parameterized Fundamental Diagrams

 
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Publication: arXiv
Volume: 2208.0088
Publication Date: 2022
Summary:

Fundamental diagrams describe the relationship between speed, flow, and density for some roadway (or set of roadway) configuration(s). These diagrams typically do not reflect, however, information on how speed-flow relationships change as a function of exogenous variables such as curb configuration, weather or other exogenous, contextual information. In this paper we present a machine learning methodology that respects known engineering constraints and physical laws of roadway flux–those that are captured in fundamental diagrams– and show how this can be used to introduce contextual information into the generation of these diagrams. The modeling task is formulated as a probe vehicle trajectory reconstruction problem with Neural Ordinary Differential Equations (Neural ODEs). With the presented methodology, we extend the fundamental diagram to non-idealized roadway segments with potentially obstructed traffic data. For simulated data, we generalize this relationship by introducing contextual information at the learning stage, i.e. vehicle composition, driver behavior, curb zoning configuration, etc, and show how the speed-flow relationship changes as a function of these exogenous factors independent of roadway design.

Authors: Thomas MaxnerDr. Andisheh Ranjbari, James Koch, Vinay Amatya, Chase Dowling
Recommended Citation:
Koch, J., Maxner, T., Amatya, V.C., Ranjbari, A., & Dowling, C.P. (2022). Physics-informed Machine Learning of Parameterized Fundamental Diagrams.
Paper

Challenges in Credibly Estimating the Travel Demand Effects of Mobility Services

 
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Publication: Transport Policy
Volume: 103
Pages: 224-235
Publication Date: 2021
Summary:

Mobility services including carsharing and transportation network company (TNC) services have been growing rapidly in North America and around the world. Measuring the effects of these services on traveler behavior is challenging because the results of any such analysis are sensitive to how (1) outcomes are measured and (2) counterfactuals are constructed. The lack of good control groups or randomization of assignment leaves lingering uncertainty over the contributions of selection bias and treatment effects to reported differences in travel behavior between users and non-users of these services. This paper reports on two approaches for measuring the effects of mobility service adoption on travel rate and car ownership. We first tried a pretest-posttest randomized encouragement experiment to deal with the shortcomings of poor control groups. Then, we turned to the approach of self-reported effects based on hypothetical controls to investigate whether variations in survey question presentation could influence respondents’ answers and thus lead to changes in estimated effects. The data to conduct this study came from two sources: a panel survey administered by the authors at the University of Washington (UW), and a survey by Populus Technologies, Inc. (Populus). Various statistical tests were applied to analyze the data, and the results highlight the pivotal role that the research design plays in influencing the outcomes, and manifest the fundamental challenge of establishing credible estimates of the causal effects of adopting mobility services on travel behaviors.

Authors: Dr. Andisheh Ranjbari, Xiao Wen, Fan Qi, Regina R. Clewlow, Don MacKenzie
Recommended Citation:
Xiao Wen, Andisheh Ranjbari, Fan Qi, Regina R. Clewlow, Don MacKenzie. Challenges in credibly estimating the travel demand effects of mobility services. Transport Policy, (103:224-235) 2021. https://doi.org/10.1016/j.tranpol.2021.02.001.