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on Transport Economics |
| By: | Miquel-Àngel Garcia-López (Universitat Autònoma de Barcelona & IEB); Ilias Pasidis (Institut d’Economia de Barcelona (IEB)); Elisabet Viladecans-Marsal (Universitat de Barcelona & IEB) |
| Abstract: | Using data from the 545 largest European cities, we study whether the expansion of their highway capacity provides a solution to the problem of traffic congestion. Our results confirm that in the long run, and in line with the ’fundamental law of highway congestion’, the expansion in cities of lane kilometers causes an increase in vehicle traffic that does not solve urban congestion. We disentangle the increase in traffic due to the increases in coverage and in capacity. We further introduce road pricing and public transit policies in order to test whether they moderate congestion. Our findings confirm that the induced demand is considerably smaller in cities with road pricing schemes, and that congestion decreases with the expansion of public transportation. |
| Keywords: | Congestion, highways, Europe, cities |
| JEL: | R41 R48 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ieb:wpaper:doc2020-11&r=all |
| By: | Xiaoyan Wang; Xi Lin; Meng Li |
| Abstract: | Autonomous vehicles (AVs) have the potential of reshaping the human mobility in a wide variety of aspects. This paper focuses on a new possibility that the AV owners have the option of "renting" their AVs to a company, which can use these collected AVs to provide on-demand ride services without any drivers. We call such a mobility market with AV renting options the "AV crowdsourcing market". This paper establishes an aggregate equilibrium model with multiple transport modes to analyze the AV crowdsourcing market. The modeling framework can capture the customers' mode choices and AV owners' rental decisions with the presence of traffic congestion. Then, we explore different scenarios that either maximize the crowdsourcing platform's profit or maximize social welfare. Gradient-based optimization algorithms are designed for solving the problems. The results obtained by numerical examples reveal the welfare enhancement and the strong profitability of the AV crowdsourcing service. However, when the crowdsourcing scale is small, the crowdsourcing platform might not be profitable. A second-best pricing scheme is able to avoid such undesirable cases. The insights generated from the analyses provide guidance for regulators, service providers and citizens to make future decisions regarding the utilization of the AV crowdsourcing markets for serving the good of the society. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2102.07147&r=all |
| By: | Nael Alsaleh; Bilal Farooq; Yixue Zhang; Steven Farber |
| Abstract: | In light of the increasing interest to transform the fixed-route public transit (FRT) services into on-demand transit (ODT) services, there exists a strong need for a comprehensive evaluation of the effects of this shift on the users. Such an analysis can help the municipalities and service providers to design and operate more convenient, attractive, and sustainable transit solutions. To understand the user preferences, we developed three hybrid choice models: integrated choice and latent variable (ICLV), latent class (LC), and latent class integrated choice and latent variable (LC-ICLV) models. We used these models to analyze the public transit user's preferences in Belleville, Ontario, Canada. Hybrid choice models were estimated using a rich dataset that combined the actual level of service attributes obtained from Belleville's ODT service and self-reported usage behaviour obtained from a revealed preference survey of the ODT users. The latent class models divided the users into two groups with different travel behaviour and preferences. The results showed that the captive user's preference for ODT service was significantly affected by the number of unassigned trips, in-vehicle time, and main travel mode before the ODT service started. On the other hand, the non-captive user's service preference was significantly affected by the Time Sensitivity and the Online Service Satisfaction latent variables, as well as the performance of the ODT service and trip purpose. This study attaches importance to improving the reliability and performance of the ODT service and outlines directions for reducing operational costs by updating the required fleet size and assigning more vehicles for work-related trips. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2102.08256&r=all |
| By: | Hoogendoorn, Y.N.; Dalmeijer, K. |
| Abstract: | For a variety of routing and scheduling problems in aviation, shipping, rail, and road transportation, the state-of-the-art solution approach is to model the prob- lem as a set covering type problem and to use a branch-price-and-cut algorithm to solve it. The pricing problem typically takes the form of a Shortest Path Problem with Resource Constraints (SPPRC). In this context, valid inequalities are known to be `robust' if adding them does not complicate the pricing problem, and `non- robust' otherwise. In this paper, we introduce `resource-robust' as a new category of valid inequalities between robust and non-robust that can still be incorporated without changing the structure of the pricing problem, but only if the SPPRC includes specic resources. Elementarity-robust and ng-robust are introduced as widely applicable special cases that rely on the resources that ensure elementary routes and ng-routes, respectively, and practical considerations are discussed. The use of resource-robust valid inequalities is demonstrated with an application to the Capacitated Vehicle Routing Problem. Computational experiments show that re- placing robust valid inequalities by ng-robust valid inequalities may result in better lower bounds, a reduction in the number of nodes in the search tree, and a decrease in solution time. |
| Keywords: | Resource-Robust, Valid Inequalities, Branch-Price-and-Cut. |
| Date: | 2021–01–12 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:134553&r=all |
| By: | Léa Bou Sleiman (Ecole Polytechnique and CREST) |
| Abstract: | This paper evaluates the impact of the downtown "Georges Pompidou" riverbank closure in 2016 on the Parisian ring road traffic conditions. Using high-resolution hourly data and a difference-in-difference design, I show that the closure increased the probability of congestion on ring road lanes with the same flow direction as the riverbank by 15%, translating into an additional 2 minutes spent on a 10 km trip. Train use and pollution data suggest that (i) only a small fraction of affected commuters switched to public transportation and (ii) a majority of affected residents suffered from a decrease in air quality. |
| Date: | 2021–02–08 |
| URL: | http://d.repec.org/n?u=RePEc:crs:wpaper:2021-03&r=all |
| By: | Norbert Brändle (Austrian Institute Of Technology) |
| Abstract: | This paper describes methods to identify trip details, including the mode of transport for each trip, from smartphone app data and from mobile network data. Use cases include travel demand surveys, travel behaviour gamification, mobility-as-a-service and automated ticketing. In the context of transport planning, the paper examines solutions to protect privacy and to enhance the representativeness of mobile phone data samples. It makes recommendations to overcome the many obstacles involved, in particular the scarcity of annotated training data. |
| Date: | 2021–01–27 |
| URL: | http://d.repec.org/n?u=RePEc:oec:itfaab:2021/07-en&r=all |
| By: | Imane Essadeq (SYSTRA); Thibault Janik (SYSTRA) |
| Abstract: | Transport planners see an opportunity in mobile phone data to better map trip destinations and monitor travel demand over time. However, such data require extensive processing to reveal trip details and transport modes. This paper defines quality indicators for reliable trip data collection and examines sensitivity to key parameters. It compares the trip matrices resulting from mobile network data with independent sources. This paper concludes on the strengths and weaknesses of such data in various transport planning tasks. |
| Date: | 2021–01–27 |
| URL: | http://d.repec.org/n?u=RePEc:oec:itfaab:2021/04-en&r=all |
| By: | Patrick Bonnel (ENTPE) |
| Abstract: | This paper proposes the estimation of trip origin-destination matrices using big data through two case studies. In the first, trip matrices are estimated from mobile network data and compared with household travel survey results. In the second, public transport trip matrices are derived from smart card data and compared with passenger survey data. The paper concludes that sample size and longitudinal data collection are big data’s main strengths, yet are limited by privacy protection constraints and by the need to control for biases in the sample. |
| Date: | 2021–01–27 |
| URL: | http://d.repec.org/n?u=RePEc:oec:itfaab:2021/06-en&r=all |
| By: | Luis Willumsen (Nommon Solutions and Technologies) |
| Abstract: | This paper guides transport planners in making the best use of mobile phone traces, derived either from mobile network data or from smartphone app data. It suggests combining such new data sources with conventional travel surveys whose sample size and cost could ultimately be reduced. In the context of a rapidly evolving mobility landscape, with new modes and new services available, big data can help monitor behaviour change, learn from quasi-experiments and develop next-generation travel demand modelling tools. |
| Date: | 2021–01–27 |
| URL: | http://d.repec.org/n?u=RePEc:oec:itfaab:2021/05-en&r=all |
| By: | André de Palma; Shaghayegh Vosough (Université de Cergy-Pontoise, THEMA) |
| Abstract: | The outbreak of SARS-CoV-2 has led to the COVID-19 pandemic in March 2020 and causes over 2 million deaths worldwide (by January 2021). Besides the public health crisis, the infection affected the global economy as well. It also led to change in people's lifestyles, amount of teleworking and teleshopping, mode choice preference, the value of time, etc. In addition to these short-term changes during the COVID-19 outbreak, this drastic transformation of the world might account for the potentially disruptive medium- and long-term impacts. Recognizing the adverse effects of the COVID-19 pandemic is crucial in mitigating the negative behavioral changes that directly relate to psychological well-being. It is important to stress that citizens and government face an uncertain situation since nobody knows the exact parameters, which explain congestion or when the vaccine will be distributed (and its efficiency, for example, with respect to mutations). The major sources of uncertainty in the context of mobility, which have an impact on short-run (route, departure time, and mode used), medium-run (car ownership), and long-run (location of job, residential location, and choice of job) mobility, are mostly listed in this paper. |
| Keywords: | COVID-19, Mobility, Housing, Teleworking, Teleshopping, Residentia location, Heath. |
| JEL: | H12 H84 I14 R4 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:ema:worpap:2021-06&r=all |
| By: | Ariel Goldszmidt (Lyft); John A. List (University of Chicago - Department of Economics; NBER); Robert D. Metcalfe (Boston University - Questrom School of Business; NBER); Ian Muir (Lyft); V. Kerry Smith (Arizona State University - W.P. Carey School of Business; NBER); Jenny Wang (Lyft) |
| Abstract: | The value of time determines relative prices of goods and services, investments, productivity, economic growth, and measurements of income inequality. Economists in the 1960s began to focus on the value of non-work time, pioneering a deep literature exploring the optimal allocation and value of time. By leveraging key features of these classic time allocation theories, we use a novel approach to estimate the value of time (VOT) via two large-scale natural field experiments with the ridesharing company Lyft. We use random variation in both wait times and prices to estimate a consumer's VOT with a data set of more than 14 million observations across consumers in U.S. cities. We find that the VOT is roughly $19 per hour (or 75% (100%) of the after-tax mean (median) wage rate) and varies predictably with choice circumstances correlated with the opportunity cost of wait time. Our VOT estimate is larger than what is currently used by the U.S. Government, suggesting that society is under-valuing time improvements and subsequently under-investing public resources in time-saving infrastructure projects and technologies. |
| JEL: | D0 D1 R4 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:bfi:wpaper:2020-179&r=all |
| By: | Xu, Lili; Lee, Sang-Ho |
| Abstract: | This study investigates the effect of port integration in a mixed oligopoly framework where a public port compete with private ports under price competition. We formulate two integration models, A-integration and B-integration, in which the public port integrates with its neighboring private port or with a non-adjacent private port, respectively. We demonstrate that the effects of A-integration (B-integration) will (not) depend on the gross consumer benefit of the cargo shipment B-integration always makes society better off. We then examine an endogenous port integration game and show that both integration and competition are Nash equilibria under the appropriate government side payments, while B-integration can be socially desirable under public finances. |
| Keywords: | Port integration; Mixed oligopoly; Public ownership; Private ownership; Endogenous port integration |
| JEL: | D43 L44 L91 R48 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:106127&r=all |
| By: | Kono, Tatsuhito; Yoshida, Jun |
| Abstract: | The Travel Cost Method (TCM) is a typical benefit measurement method, using the fact that people substitute the benefit of visiting some sites for their travel cost. However, in the case of tourist sites, travelers do not choose the number of days spent in a tourist city as continuous numbers but integer numbers. We investigate how a bias could arise from ignoring integer numbers of nights in TCM. We derive the formula of what factors constitute the bias. Next, we numerically show that when measuring benefits of improving quality at sites, the maximum bias could be around 20%. |
| Keywords: | Project Evaluation, Travel cost method, Integer property |
| JEL: | Q26 Q56 |
| Date: | 2020–03–24 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:106188&r=all |