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on Transport Economics |
| By: | Martin, Elliot PhD; Shaheen, Susan PhD; Stocker, Adam |
| Abstract: | Transportation Network Companies (TNCs) like Lyft, Uber, and their global counterparts have expanded around the world over the past decade and have changed the way that people travel around cities and regions. The individual mobility benefits provided by TNCs have been clear. Passengers can summon a vehicle quickly via smartphone from almost anywhere to take them almost anywhere, with advance communication on estimated wait time, travel time, and cost. TNCs may also provide users with added mobility benefits, especially for those living in areas where public transit service is infrequent or non-existent. However, the growing popularity of TNCs has forced important questions about their impacts on the overall transportation network. While past research has focused on many different aspects of TNC impacts, including their effects on travel behavior, modal shift, congestion, and other topics, there are still many important questions. This report advances the understanding of TNC effects on vehicle miles traveled (VMT), greenhouse gas (GHG) emissions, and personal vehicle ownership. The research also explores key questions regarding the impact of pooled TNC services, Lyft Shared rides and uberPOOL, and further investigates how TNCs alter the use of other transportation modes, including public transit. |
| Keywords: | Engineering |
| Date: | 2021–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsrrp:qt90b6d7r3&r= |
| By: | Hu, Shichun; Dessouky, Maged M. |
| Abstract: | Traffic congestion causes significant economic costs, wasted time, and public health risks. Ride-sharing, defined as a joint trip of more than two participants who share a vehicle that requires coordination among itineraries, has the potential to help mitigate congestion. A good ride-sharing system should provide quick response to passenger requests while identifying routes with minimum travel time. This is not an easy task, especially with dynamic passenger requests, variable request times, and cancelations of existing requests. One way to mitigate the effect of these uncertainties is to allow passengers to walk to a designated pick-up spot while waiting for the drivers, which can improve the system’s efficiency. Taking advantage of ride-sharing incentives such as High Occupancy Vehicle (HOV) lanes can also reduce travel time and make ride-sharing more appealing. Researchers at the University of Southern California developed a two-stage algorithm to solve the routing problem in real-time within a context where ride-sharing drivers are traveling toward their own destinations while making detours to serve passengers with flexible pickup and drop-off locations. The researchers also simulated operation of a ride-sharing system with and without HOV lanes and passenger meeting points, to determine the impact of these two factors on the operation of the system. This research brief summarizes the findings from that research and provides research implications. View the NCST Project Webpage |
| Keywords: | Engineering, Algorithms, High occupancy toll lanes, High occupancy vehicle lanes, Optimization, Ridesharing, Routes and routing, Traffic congestion |
| Date: | 2021–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt7wh3c778&r= |
| By: | Dessouky, Maged M; Hu, Shichun |
| Abstract: | The research report explored the use of High Occupancy Vehicle (HOV) lanes and meeting points in a ride-sharing system where drivers have their own origin and destination. A two-stage heuristic algorithm is proposed, which consists of an insertion heuristic to solve the pickup and delivery problem (PDP) problem and a second stage algorithm that can solve the meeting points problem optimally in polynomial time. The experimental results show that the HOV lanes and meeting points can increase the efficiency of a dynamic ride-sharing system. View the NCST Project Webpage |
| Keywords: | Engineering, Ride-Sharing, HOV Lane, Meeting Points |
| Date: | 2021–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt6qq8r7hz&r= |
| By: | van Lieshout, R.N.; van den Akker, J.M.; R. Mendes Borges; T. Druijf; Quaglietta, E. |
| Abstract: | This paper analyzes the effectiveness of decentralized strategies for dispatching rolling stock and train drivers in a railway system. Such strategies give operators a robust alternative in case centralized control fails due to an abundance of infrastructure or rolling stock disruptions or information system malfunctions. We test the performance of four rolling stock and two driver dispatching strategies in a microscopic simulation. Our test case is a part of the Dutch railway network, containing eleven stations linked by four train lines. We find that with the decentralized dispatching strategies, target frequencies of the lines are approximately met and train services are highly regular without large delays. Especially strategies that allow rolling stock to switch between lines result in a high performance |
| Keywords: | decentralized control, local dispatching, microscopic simulation, rescheduling |
| Date: | 2021–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:136996&r= |
| By: | Ali Hortacsu (University of Chicago and NBER); Olivia R. Natan (University of California, Berkeley); Hayden Parsley (University of Texas, Austin); Timothy Schwieg (University of Chicago, Booth); Kevin R. Williams (Cowles Foundation, Yale University) |
| Abstract: | We propose an approach to modeling and estimating discrete choice demand that allows for a large number of zero sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers then solves a standard discrete choice problem. We present a Bayesian IV estimation approach that addresses sampling error in product shares and scales well to rich data environments. The data requirements are traditional market-level data and measures of consumer search intensity. After presenting simulation studies, we consider an empirical application of air travel demand where product-level sales are sparse. We ï¬ nd considerable variation in demand over time. Periods of peak demand feature both larger market sizes and consumers with higher willingness to pay. This ampliï¬ es cyclicality. However, observed frequent price and capacity adjustments offset some of this compounding effect. |
| Keywords: | Discrete Choice Modeling, Demand Estimation, Zeros, Bayesian Methods, Cyclical Demand, Airline Markets |
| JEL: | C10 C11 C13 C18 L93 |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:cwl:cwldpp:2313&r= |
| By: | Mr. Serkan Arslanalp; Mr. Robin Koepke; Jasper Verschuur |
| Abstract: | This paper proposes an easy-to-follow approach to track merchandise trade using vessel data and applies it to Pacific island countries. Pacific islands rely heavily on imports and maritime transport for trade. They are also highly vulnerable to climate change and natural disasters that pose risks to ports and supply chains. Using satellite-based vessel tracking data from the UN Global Platform, we construct daily indicators of port and trade activity for Pacific island countries. The algorithm significantly advances estimation techniques of previous studies, particularly by employing ways to overcome challenges with the estimation of cargo payloads, using detailed information on shipping liner schedules to validate port calls, and applying country-specific information to define port boundaries. The approach can complement and help fill gaps in official data, provide early warning signs of turning points in economic activity, and assist policymakers and international organizations to monitor and provide timely responses to shocks (e.g., COVID-19). |
| Keywords: | Merchandise trade Data; I. Pacific island; liner shipping connectivity index; Merchandise import; supply disruption; Imports; Natural disasters; COVID-19; Trade balance; Tourism; Pacific Islands; Global; port boundary; port call; vessel data |
| Date: | 2021–08–20 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2021/225&r= |