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Ridesplitting -- a type of ride-hailing in which riders share vehicles with other riders -- has become a common travel mode in some major cities. This type of shared ride option is currently provided by transportation network companies (TNCs) such as Uber, Lyft, and Via and has attracted increasing numbers of users, particularly before the COVID-19 pandemic. Previous findings have suggested ridesplitting can lower travel costs and even lessen congestion by reducing the number of vehicles needed to move people. Recent studies have also posited that ridesplitting should experience positive feedback mechanisms in which the quality of the service would improve with the number of users. Specifically, these systems should benefit from economies of scale and increasing returns to scale. This paper demonstrates evidence of their existence using trip data reported by TNCs to the City of Chicago between January and September 2019. Specifically, it shows that increases in the number of riders requesting or authorizing shared trips during a given time period is associated with shorter trip detours, higher rates of riders being matched together, lower costs relative to non-shared trips, and higher willingness for riders to share trips., This paper has been accepted for publication in Transportation Research Part A: Policy and Practice

Studies of road pricing in which the Value of Time (VOT) varies among travelers suggest that road pricing benefits travelers with high VOT and hurts travelers with low VOT. This happens because, when a toll reduces congestion, only high-VOT travelers value the time saved more than the money cost. This paper uses a static traffic model with elastic demands to examine how the presence of a mixed-traffic (one affected by congestion) bus alternative, which is cheaper but slower than driving, alters that logic. When “agents” (potential travelers) care only about the time and money costs of each alternative, it turns out that Pareto-improving toll increases are possible; and, absent a Pareto improvement, the “Full Cost of Travel” (inclusive of time and money costs) rises only inside an intermediate interval of VOT while falling for sufficiently high and low VOT. But when agents have heterogeneous “tastes” for each mode, the Full Cost of Travel falls for all agents with VOT higher than a certain level, and below that level the direction of change depends on an agent’s taste.

This report is a guide to the practice of taxing ride-hailing at the state and local levels in the United States. The information is based on a survey of legislation, news articles, journal articles, revenue data, and interviews. We first review the literature and provide a history of ride-hailing and the practice of ride-hailing. We then profile all ride-hailing taxes in the United States, classifying these taxes according to common attributes and pointing out what details of legislation or history distinguishes each tax. One important distinction is between ad valorem taxes, levied as a percentage of fare or revenues, and “per-ride” taxes levied as a flat charge per ride. Another distinction is the differential treatment of shared and single rides. We provide extensive references to laws and ordinances as well as propose a system to classify the state legal environments under which ride-hailing is taxed. States fall into five regimes: (1) a “hands-off” regime wherein local governments are permitted wide leeway; (2) a “tax-free” regime wherein local taxes are prohibited and the state does not impose a tax; (3) a “state-tax-only” regime wherein local taxes are prohibited but the state levies taxes for its own use; (4) a “revenue-sharing” regime wherein the state levies taxes and distributes them to local governments; and (5) a “local-option” regime wherein local governments can opt into participating in a tax system regulated by the state. We make nine recommendations for Illinois policymakers considering taxes on ride-hailing, with the most important being that the state pass legislation clarifying and regulating the rights of local governments to levy such taxes.

For tolling in urban areas, the measured elasticity of demand with respect to money cost is much larger when tolls are introduced than when tolls are raised. This paper names this phenomenon “Large Elasticity at Introduction” (LEI). It has been observed in four of five cities with urban tolls: London, Singapore, Stockholm and Gothenburg. After describing these cities’ experiences, the paper discusses why LEI is surprising from a modeling perspective, and then discusses and formalizes three plausible explanations for LEI: (i) transaction costs; (ii) the “zero-price effect”; and (iii) the “price-sensitive users” theory proposed by Borjesson and Kristoffersson (2018).

This article introduces a database of bus stop spacings for 43 cities in the United States derived from GTFS files published in late 2019. Weighting each spacing by the number of times a bus traverses it, we produce distributions and summary statistics. The overall mean spacing is 313 meters. Las Vegas’ RTC has the widest mean spacing (482 m) and Philadelphia’s SEPTA the narrowest (214 m). We also compare spacings within agencies’ “core’” cities to those outside.

“Downtown congestion pricing” (DCP) is what this paper calls the practice of relieving congestion by charging tolls, or requiring the purchase of supplementary licenses, to drive in a city’s central areas. Five major cities have implemented DCP: Singapore, London, Stockholm, Milan and Gothenburg. This paper reviews the history of DCP, reporting on each system’s implementation, design, transportation impacts and finances. In addition, the paper provides historical context, discusses four recurring themes and outlines plans for future systems.

Currently, all downtown tolls are “access tolls,” meaning they charge for gross access to a zone, but tolls levied on distance-traveled are on the horizon. This paper shows how such tolls affect the distribution of trip lengths. A static model is presented in which travelers with potentially different trip lengths decide whether to drive into a downtown zone governed by a Macroscopic Fundamental Diagram (MFD), with each traveler’s choice probability declining as tolls and travel time rise. An application of Little's Law allows the model's equilibria to be derived in terms of a familiar supply/demand framework. Analysis proves and numerical simulation demonstrates that, if trip lengths and the value of a trip both vary across travelers, then access tolls inefficiently shift the distribution of car trip lengths toward long trips, whereas a distance toll can achieve the welfare-maximizing set of car trips.

Static traffic models, in the tradition of Walters (1961), typically feature a ‘‘demand curve’’ giving the vehicle flow demanded for each unit travel time (inverse speed). Traditionally, the demand curve declines because people want to drive more as travel times fall. This paper proposes that the vehicle flow demanded can, instead, plausibly rise with unit travel time (a phenomenon we call ‘‘hyperdemand’’), if congestion somehow induces some people to switch from high-to low-occupancy modes. To illustrate, we present a model of travel in an isotropic downtown where people choose among not traveling, a low-occupancy mode called ‘‘Alone’’ and a high-occupancy mode called ‘‘Pool.’’ Pool trips detour to pick up and drop off passengers en route, so congestion delays them more than Alone trips. Consequently, multiple equilibria can arise even in ‘‘light congestion,’’ and small toll increases can have dramatic impacts by eliminating equilibria.

This paper shows that severe congestion on streets controlled by traffic signals can be reduced by dynamically adapting the signal offsets to the prevailing density with a simple rule that keeps the signals’ green-red ratios invariant. Invariant ratios reduce a control policy’s impact on the crossing streets, so a policy can be optimized and evaluated by focusing on the street itself without the confounding factors present in networks. Designed for heavy traffic with spillovers, the proposed policies are adaptive and need little data - they only require average traffic density readings and no demand forecasts. A battery of numerical experiments simulating the dynamics of rush hour traffic on a congested, homogeneous circular street reveals that the proposed form of adaptation reduces the duration of the rush and overall congestion compared with pre-timed control strategies. Eighteen different adaptation policies were considered. All inspect the street densities periodically and simulataneously, and retime the signals immediately thereafter. The period is a fixed multiple of the cycle. The street is evenly divided into sections that contain a set number of consecutive blocks and signals. The offset is the same for all blocks in a section. Three inspection intervals and six section sizes were tested. The latter ranged from a single block/signal to the whole street. It was found that adaptation worked best when sections were large and adaptation frequent. The effects were considerable across all scenarios. For a short street with a short rush and high input flows the probabilistic incidence of gridlock was reduced from 10% to 0%, and the average duration of a trip from 216s to 181s. For a long street with a long rush and high input flows the gridlock probability was reduced from 23% to 0% and the average trip duration from 2037s to 1143s.

This paper considers a signalized street of uniform width and blocks of various lengths. Its signals are pretimed in an arbitrary pattern, and traffic on it behaves as per the kinematic-wave/variational theory with a triangular fundamental diagram. It is shown that the long run average flow on the street when the number of cars on the street (i.e. the street’s density) is held constant is given by the solution of a linear program (LP) with a finite number of variables and constraints. This defines a point on the street’s macroscopic fundamental diagram. For the homogeneous special case where the block lengths and signal timings are identical, all the LP constraints but one are redundant and the result has a closed form. In this case, the LP recipe matches and simplifies the so-called “method of cuts”. This establishes that the method of cuts is exact for homogeneous problems. However, in the more realistic inhomogeneous case the difference between the two methods can be arbitrarily large. The paper uses the LP method to obtain the macroscopic fundamental diagrams arising under four different traffic coordination schemes for streets with four different block length configurations. It is found that the best scheme depends on the prevailing density. Curiously, the popular scheme in which all the traffic green phases are started synchronously wins only in rare circumstances. Its performance is particularly underwhelming when the street’s blocks are long. The paper also presents density-aware numerical methods to optimize the signal offsets for 1-way and 2-way streets. For 1-way streets operated with a common cycle the method reduces to a simple graphical construction . In this case the resulting flow matches the flow that would arise if all of the street’s intersections except one with the shortest green phase had been eliminated.

A growing literature exploits macroscopic theories of traffic to model congestion pricing policies in downtown zones. This study introduces trip length heterogeneity into this analysis and proposes a usage-based, time-varying congestion toll that alleviates congestion while prioritizing shorter trips. Unlike conventional trip-based tolls the scheme is intended to align the fees paid by drivers with the actual congestion damage they do, and to increase the toll’s benefits as a result. The scheme is intended to maximize the number of people that finish their trips close to their desired times. The usage-based toll is compared to a traditional, trip-based toll which neglects trip length. It is found that, like trip-based tolls, properly designed usage-based tolls alleviate congestion. But they reduce schedule delay more than trip-based tolls and do so with much smaller user fees. As a result usage-based tolls leave most of those who pay with a large welfare gain. This may increase the tolls’ political acceptability.