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on Transport Economics |
| By: | Qian, Xiaodong; Jaller, Miguel; Xiao, Runhua; Chen, Shenyang |
| Abstract: | This project aims to know how the Intelligent Vehicle Technologies (IVT) can improve Vulnerable Road Users’ (VRU) safety in different environments and conditions (e.g., sight distance and traffic flow) at signalized intersections. For the statistical analysis on historical aggregate crash data, the project studied risk factors on crash injury severity for VRU-related crashes at signalized intersections in California cities. The researchers summarize seven critical crash types for the micro-level traffic safety simulation. For the traffic safety simulation part, it is found that Intersection Safety (INS) is empowered to be the most efficient technology to significantly reduce average collision counts for passenger cars under all seven collision types of interest. Blind Spot Detection (BSD) has the most minimal effects on those types. The safety improvement of VRU Beacon Systems (VBS) and Bicycle/Pedestrian to Vehicle Communication (BPTV) are between INS and BSD. Results show that under a certain threshold of sight distance, IVT can significantly reduce the collision probability and IVT can still improve safety under good sight condition if collisions happen in front of vehicles. In the end, the project conducted sensitive analyses of sight distance and traffic volume. For some collision types, INS and BPTV can only reduce ~50% of collision at extremely high traffic volume conditions. View the NCST Project Webpage |
| Keywords: | Engineering, Intelligent Vehicle Technologies, Vulnerable Road User, Traffic Safety, Micro Simulation |
| Date: | 2022–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt7j7414q9&r= |
| By: | Ghafelebash, Ali; Razaviyayn, Meisam; Dessouky, Maged |
| Abstract: | With rapid population growth and urban development, traffic congestion has become an inescapable issue in large metropolitan regions. Research studies have proposed different strategies to control traffic, ranging from roadway expansion to transportation demand management programs. Among these strategies, congestion pricing and incentive offering schemes have been widely studied as reinforcements for traffic control in traditional traffic networks where each driver is a “player” in the network. In such a network, the “selfish” behavior of individual drivers prevents the entire network to reach a socially optimal operation point. In future mobility services, on the other hand, a large portion of drivers/vehicles may be controlled by a small number of companies/organizations. In such a system, offering incentives to organizations can potentially be much more effective in reducing traffic congestion rather than offering incentives directly to drivers. This research project studies the problem of offering incentives to organizations to change the behavior of their individual drivers (or individuals using their organization’s services). The incentives are offered to each organization based on their aggregated travel time loss across all their drivers. This step requires solving a large-scale optimization problem to minimize the system-level travel time. We propose an efficient algorithm for solving this optimization problem. To evaluate the performance of the proposed algorithm, multiple experiments are conducted by Los Angeles traffic data. Our experiments show that the proposed algorithm can decrease the system-level travel time by up to 6.9%. Moreover, our experiments demonstrate that incentivizing organizations can be up to 8 times more efficient than incentivizing individual drivers in terms of incentivization monetary cost. View the NCST Project Webpage |
| Keywords: | Engineering, Social and Behavioral Sciences, New Mobility Services, Congestion Reduction, Incentive, Behavior Change, Travel Demand Management. |
| Date: | 2022–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt7x58z00c&r= |
| By: | Boriboonsomsin, Kanok; Vu, Alexander; Hao, Peng; Wei, Zhensong; Brown, Dylan; Barth, Matthew; Zhang, Yihang; Alasiri, Faisal; Vital, Filipe; Ioannou, Petros |
| Abstract: | In the last few decades, efforts to reduce emissions from heavy-duty diesel trucks (HDDTs) and their health impacts have been focused on imposing increasingly stringent emissions standards. This has led to significant advancements in emission control technologies and alternative fuel vehicle technologies. While these technologies are effective at reducing emissions from HDDTs, the turnover of the existing HDDT population to these advanced technologies would require a large amount of investment and along time. In the near term, other efforts to reduce emissions of the existing HDDTs and mitigate their impacts on communities are needed. Many studies have shown the promise of intelligent transportation systems (ITS) technologies in reducing the energy consumption and environmental footprint of people and goods movement through various means. This research is aimed at developing and evaluating eco-friendly ITS strategies for freight vehicles and traffic, with a focus on strategies that are applicable to the transportation systems in the South Coast Air Basin. Four specific strategies are examined in this research, including: 1) connected eco-driving, 2) truck eco-routing, 3) integrated traffic control, and 4) intelligent parking assist. This report describes the evaluation of each strategy, discusses results, and provide recommendations for future implementation. View the NCST Project Webpage |
| Keywords: | Engineering, Heavy-duty trucks, freight movement, intelligent transportation systems, traffic, emissions |
| Date: | 2022–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt7262s64x&r= |
| By: | Mohiuddin, Hossain; Fitch, Dillon; Handy, Susan |
| Abstract: | Bike-share systems are proliferating across the US and could expand opportunities for those most underserved by the transportation system. A deeper understanding of current bike-share users could enable the expansion of these services and their benefits to a larger population. With the aim of deepening this understanding, this study uses data from household and bike-share user surveys in the Sacramento region to perform behavioral modeling and market segmentation. The results show that although individuals with low incomes and students are less likely than other demographic groups to use bike-share, they use it more frequently if they do use it. Individuals who regularly use multiple modes of travel also use the service frequently. The initial adoption of the service by transport-disadvantaged groups can play a vital role in the continued and frequent use of the service. The market segmentation analysis shows that low-income individuals, students, and zero-car individuals use the service frequently for commuting and a variety of non-commuting purposes. The occasional users of the bike-share service are mainly those with higher incomes and individuals who have access to a personal car. Another market segment consists of non- and infrequent-personal bike users; however, that segment is using the bike-share service at a greater rate for different purposes compared to regular bicyclists. This suggests that bike-share may fill an important travel gap and act as a lever for increasing bike travel for some users. Overall, the results provide detailed bike-share market information that can be used to tailor urban transport policies. The results also suggest that if the user base for bike-share programs were expanded to reach even more low-income individuals, students, and multi-modal travelers, greater environmental sustainability benefits would be achieved. View the NCST Project Webpage |
| Keywords: | Social and Behavioral Sciences, Bicycles, vehicle sharing, bicycle travel, scooters |
| Date: | 2022–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt71h6g0td&r= |
| By: | Yuan Liang; Bingjie Yu; Xiaojian Zhang; Yi Lu; Linchuan Yang |
| Abstract: | Ridesourcing is popular in many cities. Despite its theoretical benefits, a large body of studies claimed that ridesourcing also brings externalities (e.g., inducing trips and aggravating traffic congestion). Therefore, many cities are planning to or have already enacted policies to regulate its use. However, their effectiveness or their impact on ridesourcing demand and traffic congestion is uncertain. To this end, this study applies difference-in-differences, i.e., a regression-based causal inference approach, to empirically evaluate the effect of the congestion tax policy on ridesourcing demand and traffic congestion in Chicago. It shows that this congestion tax policy significantly curtails overall ridesourcing demand. However, its impact on traffic congestion is marginal. The results are robust to the choice of time windows and data sets, alternative model specifications, and alternative modeling approaches (i.e., regression discontinuity design in time). Moreover, considerable heterogeneity exists. For example, the policy notably reduces ridesourcing demand with short travel distances, whereas such impact is gradually attenuated as the distance increases. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.01793&r= |
| By: | Berliant, Marcus |
| Abstract: | We examine the fine microstructure of commuting in a game-theoretic setting with a continuum of commuters. Commuters' home and work locations can be heterogeneous. A commuter transport network is exogenous. Traffic speed is determined by link capacity and by local congestion at a time and place along a link, where local congestion at a time and place is endogenous. The model can be reinterpreted to apply to congestion on the internet. We find sufficient conditions for existence of equilibrium, that multiple equilibria are ubiquitous, and that the welfare properties of morning and evening commute equilibria differ on a generalization of a directed tree. |
| Keywords: | Commuting; Internet traffic; Congestion externality; Efficient Nash equilibrium |
| JEL: | L86 R41 |
| Date: | 2022–06–30 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:113616&r= |
| By: | Fabienne Cantner; Nico Nachtigall; Lisa S. Hamm; Andrea Cadavid Isaza; Lennart Adenaw; Allister Loder; Markus B. Siewert; Sebastian Goerg; Markus Lienkamp; Klaus Bogenberger |
| Abstract: | In spring 2022, the German federal government agreed on a set of measures that aim at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures include among others, a nation-wide public transport ticket for 9 EUR per month and a fuel tax cut that reduces fuel prices by more than 15%. In transportation research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample of hundreds of participants as well as an analysis of traffic counts. In this second report, we update the information on study participation, provide first insights on the smartphone app usage as well as insights on the first wave results, particularly on the 9 EUR-ticket purchase intention. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.10510&r= |
| By: | Giuliano Rolle (University of Ferrara – Department of Economics and Management (Ferrara, Italy);) |
| Abstract: | To achieve carbon neutrality by 2050, the transportation sector must radically reduce its greenhouse gas emissions (GHGEs) emissions. According to earlier research, a growth of the market share of the used car market and an expansion of the lifetime of second-hand vehicles can play a crucial role in preventing a considerable amount of carbon dioxide (CO2) emissions. So, the purpose of this paper is to estimate, through a series of hedonic pricing models (HPMs), the consumers’ marginal willingness to pay (MWTP) for three environmental attributes of used cars: their level of fuel efficiency, their level of CO2 emissions, and their belonging to one of the European emission categories. To perform this analysis, two original cross-sectional datasets (the Panda and the Milan ones) were created through a scraping process of the used cars’ listings contained in the Italian version of the AutoScout24’s online advertising platform. Despite its several limitations, the implications that can be derived from this work, which has estimated a relevant consumers' MWTP for vehicles that have an improved fuel efficiency - especially in the Milan HPMs - and that comply with the highest European emission standards, and a very small or even negative MWTP for cars’ with a reduced level of CO2 emissions, is a support for higher investments in policies that will encourage the purchases of used cars with a high degree of these environmental features and, at the same time, the dismantling of the oldest and most polluting vehicles of the national fleet. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:srt:wpaper:0822&r= |
| By: | Vincent van den Berg (Vrije Universiteit Amsterdam) |
| Abstract: | We consider if a road is self-financing under flat or step tolling and optimized capacity while incorporating preference heterogeneity, bottleneck congestion and linear capacity cost. Previous work has shown that a sufficient condition for the toll revenue to equal the capacity cost is that the toll to equals the marginal external costs (MECs) of all types of user at all moments when their users travel. However, under ‘ratio heterogeneity’ between values of time (VOT) and schedule delay, an anonymous second-best coarse toll must differ from the heterogeneous MECs. This paper derives that this toll will be a weighted average of the MECs with the weights depending on the derivatives of the demand and travel cost functions. The capacity rule also has a second-best correction: the capacity is set higher than following the first-best rule to reduce the distortion from overpricing High-VOT users. This was ignored in previous work and makes self-financing less likely than previously thought, but it can still occur if Low-VOT users are much more price sensitive than High-VOT users, as this raises the toll. In our numerical model, the Low-VOT type must be almost twice as price sensitive than the High-VOT type for there not to be loss; and, typically, there is a 5% to 15% loss. Imposing self-financing only causes a small welfare loss of 0% to 1.5%. |
| Keywords: | Self-financing, road pricing, flat toll, step toll, coarse toll, heterogeneity, second best |
| JEL: | R48 D62 H23 R41 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20220045&r= |
| By: | Xize Wang (National University of Singapore) |
| Abstract: | Using U.S. nationwide travel surveys for 1995, 2001, 2009 and 2017, this study compares Millennials with their previous generation (Gen Xers) in terms of their automobile travel across different neighborhood patterns. At the age of 16 to 28 years old, Millennials have lower daily personal vehicle miles traveled and car trips than Gen Xers in urban (higher-density) and suburban (lower-density) neighborhoods. Such differences remain unchanged after adjusting for the socio-economic, vehicle ownership, life cycle, year-specific and regional-specific factors. In addition, the associations between residential density and automobile travel for the 16- to 28-year-old Millennials are flatter than that for Gen Xers, controlling for the aforementioned covariates. These generational differences remain for the 24- to 36-year-old Millennials, during the period when the U.S. economy was recovering from the recession. These findings show that, in both urban and suburban neighborhoods, Millennials in the U.S. are less auto-centric than the previous generation during early life stages, regardless of economic conditions. Whether such difference persists over later life stages remains an open question and is worth continuous attention. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.10601&r= |
| By: | Matsumoto, Deanna; Mace, Caitlin; Reeb, Tyler; O’Brien, Thomas |
| Abstract: | The Port of Los Angeles and Port of Long Beach, together referred to as the San Pedro Bay Port Complex, are an important source of regional economic activity in southern California. However, the port complex is also the single largest fixed source of air pollution in the region. In response to pressure from regulatory agencies and local communities, the two ports developed a Clean Air Action Plan in 2006. The research team assembled three case studies of programs implemented under the Clean Air Action Plan: the Technology Advancement Program, voluntary Vessel Speed Reduction programs, and the Clean Trucks Program. An additional case study featured a proposed private-sector infrastructure project: the Southern California International Gateway project. Each case study describes the program, stakeholders involved, barriers to implementation, and outcomes. These cases highlight the institutional challenges the ports face while working with a multitude of stakeholders and regulatory bodies to address both environmental sustainability and economic competitiveness. View the NCST Project Webpage |
| Keywords: | Business, Air quality management, Case studies, Environmental policy, Freight transportation, Intermodal transportation, Ports |
| Date: | 2022–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt9mr4958q&r= |
| By: | Xize Wang (University of Southern California); Greg Lindsey (University of Minnesota); Jessica E. Schoner (University of Minnesota); Andrew Harrison (San Francisco Municipal Transportation Agency) |
| Abstract: | The purpose of this research is to identify correlates of bike station activity for Nice Ride Minnesota, a bike share system in Minneapolis - St. Paul Metropolitan Area in Minnesota. We obtained the number of trips to and from each of the 116 bike share stations operating in 2011 from Nice Ride Minnesota. Data for independent variables included in models come from a variety of sources; including the 2010 US Census, the Metropolitan Council, a regional planning agency, and the cities of Minneapolis and St. Paul. We use log-linear and negative binomial regression models to evaluate the marginal effects of these factors on average daily station trips. Our models have high goodness of fit, and each of 13 independent variables is significant at the 10% level or higher. The number of trips at Nice Ride stations is associated with neighborhood socio demographics (i.e., age and race), proximity to the central business district, proximity to water, accessibility to trails, distance to other bike share stations, and measures of economic activity. Analysts can use these results to optimize bike share operations, locate new stations, and evaluate the potential of new bike share programs. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.10577&r= |
| By: | Velásquez, Daniel |
| Abstract: | What is the impact of improving the transit infrastructure on the gender earnings gap? How does family structure matter to un†derstand the impact of new transit infrastructure? Recent mod†els on spatial economics hinge on the assumption that house†holds are comprised of a single type of person making commut†ing and location choices. In reality, an important share of the pop†ulation lives in households with more persons, whose commut†ing choices might be interlinked through the household’s budget constraint. I set up and estimate a quantitative model of city structure featuring single and married households leveraging on the introduction of a Metro line and the Bus Rapid Transit System (BRT) in Lima, Peru. My model delivers interdependent commut†ing choices within dual†earner households. This way, reduced commute times impact one partner’s commuting patterns not only by affecting her prospects, but also those of her spouse. I show that this mechanism is quantitatively important. If I ignore this mechanism, I would overestimate gains in real income by 11% and underestimate reductions in the gender earnings gap by 103%, leading to a switch in the sign of the impact of the Metro and the BRT. |
| Keywords: | Ciudades, Desarrollo urbano, Evaluación de impacto, Género, Infraestructura, Investigación socioeconómica, Movilidad urbana, Servicios públicos, Transporte, |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:dbl:dblwop:1929&r= |
| By: | S. Roderick Zhang; Bilal Farooq |
| Abstract: | To help systematically lower anthropogenic Greenhouse gas (GHG) emissions, accurate and precise GHG emission prediction models have become a key focus of the climate research. The appeal is that the predictive models will inform policymakers, and hopefully, in turn, they will bring about systematic changes. Since the transportation sector is constantly among the top GHG emission contributors, especially in populated urban areas, substantial effort has been going into building more accurate and informative GHG prediction models to help create more sustainable urban environments. In this work, we seek to establish a predictive framework of GHG emissions at the urban road segment or link level of transportation networks. The key theme of the framework centers around model interpretability and actionability for high-level decision-makers using econometric Discrete Choice Modelling (DCM). We illustrate that DCM is capable of predicting link-level GHG emission levels on urban road networks in a parsimonious and effective manner. Our results show up to 85.4% prediction accuracy in the DCM models' performances. We also argue that since the goal of most GHG emission prediction models focuses on involving high-level decision-makers to make changes and curb emissions, the DCM-based GHG emission prediction framework is the most suitable framework. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.09073&r= |
| By: | Paolo Delle Site (UNICUSANO - University Niccolò Cusano); André de Palma (CY - CY Cergy Paris Université); Karim Kilani (LIRSA - Laboratoire interdisciplinaire de recherche en sciences de l'action - CNAM - Conservatoire National des Arts et Métiers [CNAM] - HESAM - HESAM Université - Communauté d'universités et établissements Hautes Ecoles Sorbonne Arts et Métiers Université) |
| Abstract: | We study the welfare change from project and policies when consumers' behaviour is described with additive random utility models. We consider the random compensating variation mainstream approach and review the latest methodological developments. The expectation of the random compensating variation is used as a measure of the average welfare change. Without income effect, it is expressed by the monetized difference of the expectations of the maximum utilities with and without the changes in monetary costs or quality. This measure reduces for the multinomial logit model to the logsum formula. More generally, the expectation of the compensating variation can be expressed as a path-independent line integral. The rule-of-a-half is an approximation of this line integral. With income effect, the expectation of the compensating variation, both unconditional and conditional on the choices without and with the changes, is provided by one-dimensional integrals which can be computed numerically. In the conditional case, the average welfare change is attributed to those keeping and those changing alternative. The cumulative distribution function of the compensating variation allows the analysis of inequalities by extending the classical Lorenz curve and Gini coefficient. This analysis is perfomed distinctly for positive and for negative values of the compensating variation. Treatment of observed and unobserved heterogeneity is included. The survey of theoretical results is illustrated with a numerical example in the context of transportation mode choice, based on large-scale data collected in France. |
| Keywords: | compensating variation,Gini coefficient,Lorenz curve,rule-of-a-half JEL codes: D11,D30,D60,R41,R42,R48,random utility model |
| Date: | 2022–07–10 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03719025&r= |
| By: | Hamed Kazemipoor; Mohammad Ebrahim Sadeghi; Agnieszka Szmelter-Jarosz; Mohadese Aghabozorgi |
| Abstract: | In this study, two mathematical models have been developed for assigning emergency vehicles, namely ambulances, to geographical areas. The first model, which is based on the assignment problem, the ambulance transfer (moving ambulances) between locations has not been considered. As ambulance transfer can improve system efficiency by decreasing the response time as well as operational cost, we consider this in the second model, which is based on the transportation problem. Both models assume that the demand of all geographical locations must be met. The major contributions of this study are: ambulance transfer between locations, day split into several time slots, and demand distribution of the geographical zone. To the best of our knowledge the first two have not been studied before. These extensions allow us to have a more realistic model of the real-world operation. Although, in previous studies, maximizing coverage has been the main objective of the goal, here, minimizing operating costs is a function of the main objective, because we have assumed that the demand of all geographical areas must be met. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.11811&r= |
| By: | Kailai Wang (University of Houston); Xize Wang (National University of Singapore) |
| Abstract: | Whether the Millennials are less auto-centric than the previous generations has been widely discussed in the literature. Most existing studies use regression models and assume that all factors are linear-additive in contributing to the young adults' driving behaviors. This study relaxes this assumption by applying a non-parametric statistical learning method, namely the gradient boosting decision trees (GBDT). Using U.S. nationwide travel surveys for 2001 and 2017, this study examines the non-linear dose-response effects of lifecycle, socio-demographic and residential factors on daily driving distances of Millennial and Gen-X young adults. Holding all other factors constant, Millennial young adults had shorter predicted daily driving distances than their Gen-X counterparts. Besides, residential and economic factors explain around 50% of young adults' daily driving distances, while the collective contributions for life course events and demographics are about 33%. This study also identifies the density ranges for formulating effective land use policies aiming at reducing automobile travel demand. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.11056&r= |