International Conference on Transportation and Development 2022: Transportation Planning and Workforce Development

Travel behavior and mode choice are closely related to how travel demand is addressed. Those who choose to use a bike-share system rely on the availability of a bike when they arrive at a station. This study simulates the existing NYC bike-share program to estimate customers’ wait time and the number of daily bike trips as performance measures. A truck fleet service is added to the simulation environment to transfer the bikes from the high-demand to low-demand stations as a performance improvement strategy. A 24-h simulation reveals that for 65,000 daily bike trips, on average, a user waits half a minute to pick up a bike and 1.5 min to find a vacant return spot. As a result, about 6% of customers change their minds using the program due to the longer than expected wait time. The results show that with a 10-truck fleet as an optimal number of trucks, the wait time reduces by 72%, while the number of daily trips increases by 2.4%. The simulation results, along with a benefit-cost analysis, help balance supply and demand efficiently while improving customer service satisfaction.

Despite sizable efforts on post-adoption usage intention of bike-sharing systems, post-adoption intention to use e-scooters has not received enough attention in the literature. To fill this gap, in this article, we investigate the factors affecting future intention to use e-scooters over time. To this end, we employed survey data of e-scooter users from Chicago, Illinois, containing information of 1,790 e-scooter users. The data were analyzed using exploratory factor analysis and the structural equation modeling approach. We hypothesize there are relationships between the latent constructs of perceived usefulness, perceived reliability, perceived enjoyment, perceived ease of use, well-being seeker, and the future intention to use e-scooters. Moreover, the effects of individual characteristics were examined to capture the heterogeneity among different demographics. The result of this study can provide valuable insights into the influential factors governing the long-term usage of shared e-scooters.

The Federal Highway Administration’s (FHWA’s) Focused Approach to Safety identifies pedestrian and bicycle safety as an emphasis area and provides targeted efforts for promoting pedestrian and bicycle safety through financial, staff, and training support. Pedestrian and bicycle safety is also a prioritized safety area in Florida. In 2020, the Florida Local Technical Assistance Program (LTAP) hosted a pedestrian and bicycle safety best practices virtual symposium in a four-part webinar-series format, supported by FHWA Accelerating Safety Activities Program (ASAP). The purpose of this event was to equip statewide professionals with the most effective and appropriate countermeasures and best practices for improving pedestrian and bicycle safety. The event was a successful workforce development experience based on training evaluations related to number of attendees and their location distribution, attendee evaluations on webinar quality, speaker knowledge and skills, and improvements in attendee understanding of pedestrian/bicycle safety. Educational materials relevant to pedestrian and bicycle safety topics were developed to enhance training delivery. The event also provided an opportunity to verify the advantages of online training compared to in-person events in terms of increased hosting capacity and training effectiveness, better time management, and increased communication opportunities among presenters and attendees. The experience obtained and lessons learned provided significant benefits for future safety workforce development training planning and coordination.

Installing safe, accessible, and cost-effective bicycle and pedestrian infrastructure contributes to active transportation, public health, and environmental benefits. An understanding of Departments of Transportation (DOTs) perspective on active transportation related policies and best practices helps to address the challenges and proactively plan/build bicycling/pedestrian friendly environments. The focus of this research is to survey the staff of state DOTs and share policies and best practices associated with active transportation. A survey questionnaire was developed with 20 questions, to capture quantitative and qualitative aspects, and gather information from the staff of state DOTs. Of the 50 state DOTs, responses were received from 13 state DOTs. The survey responses were used to understand funding allocated for bicycle and pedestrian projects, and identify key barriers, policies, and best practices. Priorities, hindrances/barriers, and opportunities pertaining to finance, policies, and practices are summarized. The findings and recommendations are useful to the state DOTs and local agencies to improve active transportation.

The social force model (SFM) is a widely used method to predict pedestrian behavior. This paper develops a modified social force model to understand pedestrians’ behavior at a signalized crosswalk with only autonomous vehicles (AVs) on the road. Previous research indicated that pedestrians are likely to feel less safe around driverless “ghost vehicles” like AVs, and a repulsive behavior of the pedestrians is observed toward AVs. Hence, a new repulsive force is incorporated into the traditional SFM to account for this phenomenon, and the modified SFM’s principle used to simulate pedestrian behavior. Simulation of pedestrian behavior is performed using the open-source crowd simulation software, VADERE. Simulation results show that pedestrians’ walking behavior becomes chaotic indicating that pedestrians may require more time to cross the road in this scenario. The paper discusses the results of these simulation experiments and their implications on transportation engineering.

Many believe that telecommuting could be a solution for some of the significant adverse impacts of our transportation systems, e.g., traffic congestion, greenhouse gas and air pollution emissions, and energy consumption. Observations may have further strengthened this belief during the first year of the COVID-19 Pandemic, where streets were deserted and clean air and wildlife returned to urban areas. Accordingly, this study investigates the legitimacy of this belief. The NHTS 2017 data set was used to examine the travel activity of commuting workers against telecommuters. Workers were classified into one of five telecommute classes based on primary work location, telecommute engagement, logged trips, and option to telecommute: home-based workers (those who work predominantly from home), primary and ancillary telecommuters (those who telecommute), passive telecommuters (those who have the option to telecommute), and non-telecommuters. The various forms of telecommuting were found to significantly impact average daily trip counts and average daily trip miles produced in both urban and rural contexts. Contrary to the possibly traditional belief, telecommuters made more trips per day and traveled longer distances per day compared to non-telecommuters. Additionally, the study investigated the differences in trip rates by trip purpose for each of the five telecommuting classes. The analysis revealed that while home-based work (HBW) trips for primary telecommuters decreased significantly, all other trip purposes increased (in number and distance) and in a higher manner than the decrease of the HBW trips. These findings indicate that telecommuting is likely to increase total VMT and associated negative impacts and should inform relevant transportation policies.

The Oregon State University (OSU) research team investigated the optimization of striping configurations of heavy vehicle parking at rest areas to improve their utilization rate. The selected site for this study was the I-5 Northbound Santiam Rest Area, which is composed of 12 heavy vehicle parking spots angled at 45 degrees with conflicting, adjacent passenger car parking. The Oregon Department of Transportation (ODOT) provided the OSU research team with two striping alternatives. These alternatives and the existing conditions were modeled in the OSU Heavy Vehicle Driving Simulator, which were tested via an experimental workshop with four experienced drivers—each containing an Oregon commercial driver’s license and at least one year of experience. For each of three scenarios, participants executed three parking maneuvers, where each increased in level of difficulty. Collectively, the findings of this study suggest for the heavy vehicle parking lot to be restriped such that the entrance aisle width is at least 20 ft, parking spots to be 1–2 ft wider, and adjacent passenger car parking to be removed.

Innovative interchange designs have been considered as an effective solution for increasing traffic demand all over the United States. However, innovative interchange designs have only gained interests from transportation management agencies in the last couple of decades, and therefore research related to innovative interchanges are scarce compared to that of intersections and other transportation assets. To examine the effectiveness and weaknesses of innovative interchanges, this study compares the operations of traditional diamond interchange with three innovative interchanges: single point interchange, diverging diamond interchange, and displaced left-turn interchange. Under the same traffic condition and similar design capacity, results indicate that innovative interchanges outperform traditional diamond interchange in all scenarios when comparing their level of service. Furthermore, the single point interchange provides outstanding operational performance compared to other interchanges. Results also show a considerable advantage of the single point interchange, which is the ability to provide balanced delays among the approaches of the interchange.

The goal of transportation asset management is to maximize the performance of available assets. Adequate visualization of assets, traffic demand, safety, and other conditions gives a better understanding of the complexity of the existing situation; however, most agencies lack a single tool to visualize such transportation-related information and prepare improvement plans. A prototype data visualization and analysis tool has been created for the city of Columbus, Ohio. It leverages GIS and other reporting tools to capture and show information that is relevant and matters to the people being served. This study demonstrates data visualization examples that utilize data from multiple sources throughout the city to support asset management, enhance the quality of life, and improve traffic safety (as part of the city’s Vision Zero initiative), among other things. It demonstrates the use of tools to visually present transportation assets, their valuation, safety, equity; prepare work plans; and envision the effect of the recommended work plan on predicted transportation network conditions.

Over the past decades, different kinds of surveys have traditionally been the primary data source for understanding travel demand (patterns and behaviors) in a region, developing transportation planning models, and designing transportation infrastructure. However, the substantial evolution of communication technologies and the superior market penetration of smartphones over the last decade have opened the door for novel types of data: cellphone trace big data. While traditional surveys will continue to provide value and answers that are not possible by cellphone trace big data, applications of this novel data source in transportation have been consistently growing and are expected to grow further. The proposed study will utilize cellphone trace big data (from Streetlight Insight) to uncover the spatio-temporal distribution of travel demand (e.g., trips by vehicle) in urban and rural areas in Fresno County, California. The study will visualize origin-destination (OD) patterns in the region and contrast them with the existing transportation infrastructure. The study will demonstrate the potential value of this novel data source as it provides additional and valuable information that can significantly improve our ability to understand travel demand and plan and design more efficient transportation systems to meet this travel demand.

National travel surveys capture travel behaviors, patterns, and trends of inhabitants of their respective nations, and are valuable tools for transportation planning, modeling of travel demand and transportation impacts, analysis of transportation systems, and development of efficient transportation policies. This study utilizes national travel surveys of three nations to compare and analyze travel behavior and trends across the three of them. Great Britain (GB) conducted the very first travel survey in 1965, and the United States (US) and New Zealand (NZ) followed suit in 1969 and 1989, respectively. The main purpose of this research is to compare travel behaviors across these three nations. This analysis was conducted for each of the three nations’ 2017 travel survey. The examined travel measures include travel activity (trips/person/day), travel distance (miles/person/day), average trip distance (miles/trip), travel time (minutes/person/day), average trip time (min/trip), and modal split. These computed travel measures revealed significant differences between the three nations; for example, GB had the lowest travel activity, and the US had the longest travel distance and time. Additional attributes were collected to capture the demographic and geographic differences between these nations, e.g., population density. Some of the observed travel behavior differences may be attributed to differences in the country sizes, population age distributions, population densities, and transportation mode infrastructure. In addition to these 2017 comparisons, travel trends between 1995 and 2017 were compared for both the US and GB. The analysis revealed additional differences in travel activity trends between these two nations during this time period. This study and these findings indicate the potential benefit of analyzing travel differences between nations to improve our understanding of travel behavior and plan for more sustainable transportation systems.

The COVID-19 pandemic has impacted a wide range of human activities, from food delivery habits to major moving and travel decisions. Results indicate multiple pandemic-related factors have influenced millions of relocation decisions by Americans (e.g., health risk, financial pressures, more space, and employment), and there are various positive economic and social outcomes of this influence (e.g., remote work and education), enabling more affordable living and opportunity. This paper addresses COVID-19 impacts on mobility, especially involving permanent relocations. Survey design and data analysis with U-Haul targeted customers in Austin, New York, San Diego, and Chicago to understand mobility, new moving dynamics, and motivations.

With the development of the Internet of Things, individual independent network systems are gradually combined into multi-layer integrated network systems. The disturbance events affect the single system network and correlate the performance of the whole network system. The superposition of different disturbance events can have a severe impact on the performance of the entire network system, so it is important to study the disturbance impact of multi-layer network systems under the superposition of unexpected events. In this paper, we establish a multi-layer network system resilience system under multiple event disturbances based on complex network theory, using multi-layer network characteristic indicators as the measurement indexes and combining various disorders. The multi-layer network resilience system provides the basis for emergency management measures under emergency events.

Safe-Routes-to-School (SRTS) programs have gained national and international interest in the past decade to address the sharp decline in the percentage of grades K–8 students that use active modes, i.e., walking or biking, to travel to school. Such programs do not typically lack policy guidelines on implementation management at the high federal or state agencies’ level. However, at a finer scale of regional agencies or localities, SRTS programs suffer from immaturity in the state of practice, especially in the preliminary efforts to initiate the program. In this paper, concerted efforts of agency (Hampton Roads Transportation Planning Organization) and locality (city of Hampton) will be presented to showcase best practices on patterning and benchmarking school zones for programs’ implementation management. Guidelines on data collection and acquisition, processing and key performance indicators’ development, multi-criteria evaluation, and ranking will be presented to fill this practice gap. Also, partnering lessons learned on identification of opportunities to fund those programs, like the Transportation Alternatives Set-Aside Program, will be discussed at the conclusion section.

Measurement of driving comfort is necessary, as an uncomfortable journey experience could be extremely hazardous for old people and patients. Overall driving comfort depends on several environmental contexts in addition to the driving behaviour. In this paper, we present QDCL, an android application, capable of measuring context specific driving comfort. RMS values of the tri-axial acceleration values followed by FFT have been considered for defining driver behaviour-based comfort index (CI). For defining the context specific comfort index (CSCI), road condition and ambient temperature have been considered along with CI. International roughness index (IRI) has been considered for quantifying road anomaly, which can affect driving comfort. Deviation of ambient temperature from ideal temperature range of human body has also been considered for measuring CSCI. QDCL measures CSCI for each 20 s interval. The application provides an alarm to the driver in case the CI value exceeds the threshold for “a little uncomfortable” journey experience.

The coronavirus pandemic had a significant effect on traffic volumes; the closures lead to a change in travel patterns and driver behaviors. This study evaluates the trip generation associated with commercial properties that remained open using a before, during, and post-pandemic lockdown approach. The study identified an anomaly in trip generation during the height of the pandemic pertaining to a specific commercial property type, where trips increased by 12% and 56% in the AM and PM periods. The results during the post-pandemic period were found to remain elevated at a rate of 8% to 36% higher in the AM and PM periods, and indicate a lasting change in driver behavior. This change should be considered when conducting future traffic studies or using data collected during the pandemic. An adjustment factor was also developed to adjust data collected during the pandemic and current post-pandemic lockdown times.

Geospatial location service is not only used in measuring ground distances and mapping topography, but has also become vital in many other fields such as aerospace, aviation, natural disaster management, and agriculture, to name but a few. The innovative and multi-disciplinary applications of geospatial data drive technological advancement toward precise and accurate location services available in real-time. Although the RTN technology is currently utilized in a few industries such as precision farming, construction industry, and land survey, the implications of precise real-time location services would be far-reaching and critical to many advanced transportation applications. The GNSS real-time network (RTN) technology, introduced in the mid-1990s, is promising in meeting the needs of automation in most of the advanced transportation applications. This article presents an overview of the GNSS-RTN technology, its current applications in transportation-related fields, and a perspective on the future use of this technology in advanced transportation applications.

States and federal administrations in America provide regulations and incentives to promote the utilization of alternative fuels. The contents and effects of such regulations and incentives have yet been explored to a great extent. This study evaluates the content and impact of the incentives and regulations on electric vehicle (EV) acquisitions using text mining and the negative binomial (NB) regression. Findings indicate that western states have a relatively higher number of EVs per million residents. Moreover, the NB results show that rebates and grants are associated with more EVs. On the other hand, exemptions and tax incentives are associated with lower EVs acquired. Loan incentives are associated with an increase in the acquisition of EVs but are statistically insignificant. Furthermore, air quality and emissions-related regulations are associated with the increased acquisition of EVs. The findings may assist agencies in identifying best practices and policies to promote alternative fuels.

Many communities across the country face transportation equity issues. It has become a primary concern for transportation engineers and planners in most projects. There is, however, relatively shallow understanding of how equity biases present in the critical data sources that are used as the basis for important decisions. This paper fills this research gap by understanding the equity implications of different data sets. This paper assesses the equity of inductance loop detector data in King County in Washington State. This is done through ecological regression. In the methodology, census data from the county is connected with the freeway use data to draw conclusions about the demographics of those using the freeway. Two separate regression models are created to study racial biases and income biases in the freeway use data. While no clear pattern for racial biases in the freeway data is observed, there are biases found in the representation of different income brackets. Those of lower incomes are generally underrepresented, while those with higher income are generally overrepresented. This holds many equity implications for the equity of the freeway loop detector data set and shows that ecological regression may serve as a valid method for understanding equity biases in different transportation data sets.

The emergence of the COVID-19 pandemic in early 2020 caused drastic changes in the traffic patterns in highways throughout the country. Changes in the working conditions besides periods of enforced lockdowns generated different traffic demands, which also led to change of travel behavior. Larger demands at non-peak hours became prevalent, while lower than average demands were observed during the peak hours. One of the corridors impacted by the pandemic is I-80 in Northern California. Certain sections of this highway have a large traffic demand, specifically the section between the cities of Sacramento and Roseville. By the analysis of performance measurement system (PeMS) data source, the aim of this research is the comparison of traffic conditions and behaviors, including speed profiles and demand during peak hours in specific segments of this corridor for the two consecutive years of 2019 and 2020. The results display the impacts of the pandemic on traffic patterns and behavior during the year 2020 in the investigated highway segment. One of the main differences between the two years is lower demand and higher average speed in 2020 compared to 2019.

Transportation is the movement of people and goods over time and space. This is an important matter in our lives, and people cannot live without it. Transport enables access for people with their homes to their work, education, health services, shops, and so forth. The transportation system has many problems in developing countries such as traffic congestion, lack of reliable and safe public transportation, road accidents, and difficulties for non-motorized transport. The transportation sector is the highest responsible part of harmful environmental impacts in cities and is also responsible for high air and noise pollution and generating greenhouse gas emissions. Forming a sustainable and green urban transportation system with an integrated method is essential for policymakers and decision-makers. This system changes the path of urban transport and mobility toward a more sustainable and green future by providing an emerging affordable, economically viable, the orientation of people, and environment-friendly transport systems. The main purpose of this study is to evaluate the present condition and problems facing the transportation system in developing countries in general and particularly in Afghanistan. It also presents the Afghanistan government’s intervention in solving mobility and transportation problems. Finally, the study examines the strategies and approaches that transportation experts must use to address sustainable and green transportation.

In 2017, the town of Innisfil, Ontario, launched Innisfil Transit in partnership with Uber, a transportation network company, to provide a subsidized on-demand public mobility service as an alternative to investing in a new fixed-route bus service. The performance of Innisfil Transit is documented in a 2021 Ryerson University report by Sweet, Mitra, and Benaroya, which shows greater cost effectiveness of the mobility provided over the proposed bus alternative. This paper expands on those findings by assessing Innisfil Transit with respect to sustainability, scalability, and resiliency. First, we quantify the energy and emissions of this program relative to traditional transit and driving alone across varying powertrains. We then characterize a conservative first-order estimate of the percentage of US communities that fall within a similar spatial-demographic tier as Innisfil. Replicability also hinges on service cost and performance in comparison to average values for low-density transit in the US. Lastly, most transit agencies experienced a significant drop in demand (as much as 90%) with slowly rebounding ridership since the onset of the COVID-19 pandemic. The resiliency of the Innisfil program to the pressures induced by the pandemic is examined in comparison to other transit operations. The lessons learned across these three dimensions complement prior work to better understand the efficiency and sustainability of on-demand public mobility service for low-density communities like Innisfil.

The transportation sector has incorporated resilience research in their field only recently and vigorously. Yet, existing literature rarely provides a comprehensive tool to measure the resilience of the transportation infrastructure. This study aims to develop a decision-making tool to measure the resilience of the transportation infrastructure from a structural, and construction, and management perspective. A survey was developed after conducting an extensive literature review and distributed among experts. Data were statistically tested, and a resilience measuring tool was developed. It was found that the time to start reconstruction has a significant impact on the resilience of the transportation infrastructure. Delay in reconstruction work will cause significant serviceability delay, which will prolong the recovery of the affected segment of the transportation network. This tool will provide a relative resilience level, which can be used to compare the resilience of multiple networks and investment can be prioritized based on the vulnerability of the networks.

Throughout the United States, state and local Departments of Transportation (DOTs), as well as other infrastructure owners and operators (IOOs), lack the necessary connected and automated vehicle (CAV) demonstration and evaluation tools to effectively make procurement and deployment decisions. To address this need, the Federal Highway Administration (FHWA) and the Intelligent Transportation Systems Joint Program Office (ITS JPO) are working together and developing resources for deploying and maintaining the emerging CAV system. In order to gauge the effectiveness of the connected and automated vehicle education (CAVe)-in-a-box, a tool that demonstrates how a complete CAV system works, an extended outreach and scope assessment was conducted; measurable feedback on the demands and effectiveness of the system were collected through a series of virtual workshops, presentations, and alpha testing.

Women who work in the construction industry are more vulnerable than their male counterparts to accidents, some of which can be fatal. This is partially attributable to their inability to identify occupational hazards that are unique to their gender and are not usually covered in training sessions. Health and safety training programs have been shown to enable them to identify hazards in the workplace, but their effectiveness is yet to be determined. This study aims to develop materials that can be used to create an awareness of the occupational hazards that women encounter and how they can mitigate or eliminate their effects, and it will also evaluate the effectiveness of the training. Thirty-two female construction employees in Texas were trained, and their knowledge was tested prior to and immediately after the training, using a standard questionnaire that consisted of eight questions pertaining to the materials covered during the training. The responses were quantitatively analyzed, and results demonstrated a significant improvement in the knowledge of the participants after the training. These study findings can assist construction companies and policymakers in implementing safety practices that will improve the health and safety of female construction employees.

The number of deaths and minor and severe injuries in the construction industry are increasing every year and are directly related, at least in part, to the rising number of extremely hot and cold days caused by climate change. The lack of sufficient federal laws on safety enforcement for workers performing in extreme weather conditions has also contributed to the dilemma. This paper evaluates the health care costs that are associated with construction workers performing in hot and cold weather conditions by performing a descriptive analysis. A questionnaire survey was developed and distributed to a variety of workers involved in the construction sector, and 100 responses were received. An analysis of the results revealed that construction workers exposed to extreme hot and cold weather incur more health care expenses than workers in a thermo-neutral environment, male construction workers spend more on medical care than females, health care costs increase as workers age, and those who have higher medical bills suffer from more health issues. The results of this study will help construction professionals understand the health care cost claims of workers performing in extreme weather conditions.

More than one-third of all occupational deaths are heat-related, and it is probable that many more non-fatal injuries occur from overexposure to heat but are not reported. Prolonged exposure to excessive heat results in construction workers suffering from a wide range of physical and mental health issues that can lower their productivity and cause an increased number of accidents. To investigate how heat impacts their physical and mental health, a survey was developed and distributed through an online platform to construction workers who work in various sectors, including transportation. The 100 responses received revealed that excessive heat affects workers both mentally (difficulty concentrating, irritableness, and frequent mood swings) and physically (fatigue, level of hydration, and kidney disease). Perspiration also plays a role in how a worker is impacted by the heat, as higher blood sugar levels and hypertension are more common among workers who perspire less. Breaks, continuous hydration, sufficient cooling facilities, and acclimatization of workers are some of the factors that can reduce heat-related injuries and accidents on construction sites.

In 2020, the Ghana Institution of Engineering conducted an online survey of engineering practitioners in Ghana to improve the ethics culture regarding engineering professional practice. The survey results portrayed a weak ethics culture in the workplace. Out of 262 practitioners, 41% indicated reluctance to report cases of ethical misconduct due to inadequate whistleblower protections and inaction of leaders to address reports. Inflating costs in budget and procurement processes, accessing personal social media platforms during work hours, reporting late to work, and failing to observe time limits set for lunch and breaks were dominant problems in the private and public sectors. The weakest link in ethical decision-making was driven primarily by rationalization. To improve workplace ethics culture, senior engineering practitioners must communicate ethics as a priority, model concurrent behavior for their employees, take swift and fair actions to address cases of ethical misconduct, and implement ongoing ethics training to foster an environment that encourages people to report ethical misconduct.

Unreliable transportation networks cause catastrophic failures in modern freight logistics. Scheduled logistics must consider the differences in traffic patterns that occur across the temporal and spatial domains of freight networks. Recent advancements in data collection (e.g., probe vehicle data) enable planners to assess discrete time periods of scheduled operations to measure changes in the reliability of transportation networks. Instinctively, infrastructure projects that improve system capacity will also benefit truck travel time reliability (TTTR); however, there is a need to develop methods that assess the influence of variations in freight-specific operating conditions. In this paper, we investigate several years of data to monitor the performance of a system that is subject to disruptions from infrastructure and environmental changes. Specifically, the TTTR performance is monitored before, during, and after capacity improvements have been constructed for a highway transportation project. The methods introduced with this paper demonstrate that a freight reliability metric has a unique response to capacity modifications that may not be otherwise discernable when measuring performance changes with traditional metrics. This approach describes how infrastructure improvements will influence freight transport reliability.

Green loading zones (GLZs) are curb spaces dedicated to the use of electric or alternative fuel (“green”) delivery vehicles. Some US cities have begun piloting GLZs to incentivize companies to purchase and operate more green vehicles. However, there are several questions to be answered prior to a GLZ implementation, including siting, potential users, and their willing to pay. We reviewed best practices for GLZs around the world and surveyed goods delivery companies operating in New York City to collect such information for a future GLZ pilot. The findings suggest the best candidate locations are areas where companies are currently subject to the most parking fines and double parking. Companies expressed willingness to pay for GLZs, as long as deploying green vehicles in the city can offset other cost exposures. Respondents also selected several single-space GLZs spread throughout a neighborhood as the preferred layout.