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Marriott Marquis, La Costa
Friday, Jan. 3, 2020 10:15 AM - 12:15 PM (PDT)
- Chair: Tobias Salz, Massachusetts Institute of Technology
The Welfare Effect of Road Congestion Pricing: Experimental Evidence and Equilibrium Implications
AbstractThe textbook policy response to traffic externalities is congestion pricing. However, quantifying the welfare consequences of pricing policies requires detailed knowledge of commuter preferences and of the road technology. I study the peak-hour traffic congestion equilibrium using rich travel behavior data and a field experiment grounded in theory. Using a newly developed smartphone app, I collected a panel data set with precise GPS coordinates for over 100,000 commuter trips in Bangalore, India. To identify the key preference parameters in my model – the value of time spent driving and schedule flexibility – I designed and implemented a randomized experiment with two realistic congestion charge policies. The policies penalize peak-hour departure times and driving through a small charged area, respectively. Structural estimates based on the experiment show that commuters exhibit moderate schedule flexibility and high value of time. In a separate analysis of the road technology, I find a moderate and linear effect of traffic volume on travel time. I combine the preference parameters and road technology using policy simulations of the equilibrium optimal congestion charge, which reveal notable travel time benefits, yet negligible welfare gains. Intuitively, the social value of the travel time saved by removing commuters from the peak-hour is not significantly larger than the costs to those commuters of traveling at different, inconvenient times.
Customer Preference and Station Network in the London Bike Share System
AbstractWe study customer preference for the bike share system in the city of London. We estimate a structural demand model on the station network to learn the preference parameters and use the estimated model to provide insights on the design and expansion of the bike share system. We highlight the importance of network effects in understanding customer demand and evaluating expansion strategies of transportation networks. In the particular example of the London bike share system, we find that allocating resources to some areas of the station network can be 10 times more beneficial than others in terms of system usage, and that the currently implemented station density rule is far from optimal. We develop a new method to deal with the endogeneity problem of the choice set in estimating demand for network products. Our method can be applied to other settings, in which the available set of products or services depends on demand.
Platform Design in Ride Hail: An Empirical Investigation
AbstractWe study optimal platform design in ride hail markets. A ride hail platform organizes a sequence of interdependent two-sided spatial markets, where trade depends on the extent of participation among both buyers and sellers. We develop an empirical framework that can capture both the spatial and inter-temporal features in such a market. To estimate the model, we use detailed consumer choice and supply data from a large European ride-hailing company. The platform operates through a unique mechanism that allows drivers to bid on trips. Driver bids generate a choice set for passengers who trade off lower fares with longer waiting times. This unique feature allows us to back out passengers willingness to pay for both price and waiting time reductions. On the driver side, the auction feature allows us to obtain detailed measures of the opportunity cost of time. We use the estimates to study several dimensions of platform design.
- R4 - Transportation Economics