Bottleneck Congestion and Modal Split Revisited

The paper examines the efficiency of alternative road pricing schemes when an alternative railroad service is available. The paper uses a model, developed by Tabuchi (1993), in which road transport presents a bottleneck congestion technology while railroad transport shows economies of scale with respect to the number of train users. The competition between the two modes is assumed to be on cost basis only. It is found that if the railroad fare is set equal to the average cost, the relative efficiency of the regimes depends on parameters' values. The numerical simulation shows that the fine toll regime is generally to be preferred to the alternative regimes but when the fixed railroad cost is large enough so that the inefficient exploitation of the scale economies is less than compensated by the toll revenue.Road transport, Public transport, Congestion, Congestion pricing

Modeling the morning commute for urban networks with cruising-for-parking: An MFD approach

This study focuses on the morning commute problem with explicit consideration of cruising-for-parking, and its adverse impacts on traffic congestion. The cruising-for-parking is modeled through a dynamic aggregated traffic model for networks: the Macroscopic Fundamental Diagram (MFD). Firstly, we formulate the commuting equilibrium in a congested downtown network where travelers have to cruise for curbside parking spaces. The cruising-for-parking would yield longer trip distance and smaller network outflow, and thus can induce severe congestion and lengthen the morning peak. We then develop a dynamic model of pricing for the network to reduce total social cost, which includes cruising time cost, moving time cost (moving or in-transit time, which is the duration during which vehicles move close to the destination but do not cruise for parking yet), and schedule delay cost. We show that under specific assumptions, at the system optimum, the downtown network should be operating at the maximum production of its MFD. However, the cruising effect is not fully eliminated. We also show that the time-dependent toll to support the system optimum has a different shape than the classical fine toll in Vickrey's bottleneck model. In the end, analytical results are illustrated and verified with numerical experiments

City Tolls – One Element of an Effective Policy Cocktail

Stadtverkehr, Verkehrsstau, Straßenbenutzungsgebühr, Stadtverkehrspolitik, Urban transport, Traffic jam, Road pricing, Urban transport policy

Incorporation of micro-level analysis in strategic urban transport modelling: with a case study of the Greater Beijing

Many developing countries and regions are suffering from severe urban transport problems arising from accidents, congestion, air pollution, rising carbon intensity, and chronic under-funding of infrastructure and services. The problems make those cities the most polluted and often the least liveable. Strategic transport modelling has been recognised as an effective approach for developing and testing policy options, especially where it is integrated with land use planning and urban design. However, in most developing-country cities strategic transport modelling has been out of reach for practical policy use because of its sophisticated data and skill requirements, which currently imply unaffordable high costs and long durations for model development. This means that strategic urban transport modelling is the least available where it is needed most urgently. Meanwhile, the spread of smart data in mapping and urban activity monitoring has often been just as rapid in developing countries as in the developed. This has triggered new approaches in micro-level analyses of transport networks, personal movements and vehicles. In the most advanced cases, the new analyses have started to influence strategic modelling. The main hypothesis of this dissertation is that an incorporation of the micro-level smart data and analyses in strategic urban transport modelling will make it feasible to establish a sufficiently robust strategic transport model for evidence-based policy analysis with cost, time and skill thresholds that are close to being affordable in developing country cities. In order to test this main hypothesis, a number of novel model development tasks have been carried out which contribute to the field of applied urban modelling. This new approach aims to contribute to the transformation of the prevailing modus operandi where model development could not start in earnest until extensive data collection and skills training have been completed to a situation where a sufficiently robust model can be established cheaply and quickly to support on-going and incremental refinements. More specifically, new modelling tools have been developed as part of this dissertation using sparse GPS taxi traces to identify slow-moving and stopping traffic hotspots using an extended density-based spatial clustering algorithm that is tolerant of significant data noise, and to estimate congested road speeds (which used to be very costly and time-consuming to obtain if at all). The micro-level network, congested speeds and insights into the nature of the congested traffic have been incorporated into a MEPLAN-based strategic transport model interacting with a MEPLAN-based land use and travel demand model. This means that the strategic economic, social and environmental impacts of transport interventions can be tested in a robust way through accounting for the interactions among transport, land-use and background social-technical trends. A new approach to establish the medium to long term visions for alternative travel demand management and transport investment scenarios has been tested using this model. The methods and algorithms have been tested in a case study of the Greater Beijing region, which consists of the municipalities of Beijing and Tianjin together with the surrounding areas in the province of Hebei. The government’s data regulations of restricting overseas studies to using only publicly available data sources have made the case study ideal for testing the new approach. The potential of the new strategic urban transport model has been tested through a wide range of policy scenarios. The results suggest that the new approach developed in this dissertation has made it not only cheaper and faster to develop a robust model, but could also potentially fill a gap in the lack of medium to long term perspectives regarding major road and metro investments over the next two decades. Such analyses could be of critical importance in improving the performance of the transport system in terms of safety, economic efficiency, air quality and carbon reduction given the long lead times to plan and deliver transport infrastructure investments

AN INTEGRATED SCORE-BASED TRAFFIC LAW ENFORCEMENT AND NETWORK MANAGEMENT IN CONNECTED VEHICLE ENVIRONMENT

The increasing number of traffic accidents and the associated traffic congestion have prompted the development of innovative technologies to curb such problems. This dissertation introduces a novel Score-Based Traffic Law Enforcement and Network Management System (SLEM), which leverages connected vehicle (CV) and telematics technologies. SLEM assigns a score to each driver which reflects her/his driving performance and compliance with traffic laws over a predefined period of time. The proposed system adopts a rewarding mechanism that rewards high-performance drivers and penalizes low-performance drivers who fail to obey traffic laws. The reward mechanism is in the form of a route guidance strategy that restricts low-score drivers from accessing certain roadway sections and time periods that are strategically selected in order to shift the network traffic distribution pattern from the undesirable user equilibrium (UE) pattern to the system optimal (SO) pattern. Hence, it not only incentivizes drivers to improve their driving performance, but it also provides a mechanism to manage network congestion in which high-score drivers experience less congestion and a higher level of safety at the expense of low-performing drivers. This dissertation is divided into twofold. iv First, a nationwide survey study was conducted to measure public acceptance of the SLEM system. Another survey targeted a focused group of traffic operation and safety professionals. Based on the results of these surveys, a set of logistic regression models was developed to examine the sensitivity of public acceptance to policy and behavioral variables. The results showed that about 65 percent of the public and about 60.0 percent of professionals who participated in this study support the real-world implementation of SLEM. Second, we present a modeling framework for the optimal design of SLEM’s routing strategy, which is described in the form of a score threshold for each route. Under SLEM’s routing strategy, drivers are allowed to use a particular route only if their driving scores satisfy the score threshold assigned to that route. The problem is formulated as a bi-level mathematical program in which the upper-level problem minimizes total network travel time, while the lower-level problem captures drivers’ route choice behavior under SLEM. An efficient solution methodology developed for the problem is presented. The solution methodology adopts a heuristic-based approach that determines the score thresholds that minimize the difference between the traffic distribution pattern under SLEM’s routing strategy and the SO pattern. The framework was applied to the network of the US-75 Corridor in Dallas, Texas, and a set of simulation-based experiments was conducted to evaluate the network performance given different driver populations, score class aggregation levels, recurrent and non-recurrent congestion scenarios, and driver compliance rates

Dynamic and Static congestion models: A review

We begin by providing an overview of the conventional static equilibrium approach. In such model both the flow of trips and congestion delay are assumed to be constant. A drawback of the static model is that the time interval during which travel occurs is not specified so that the model cannot describe changes in the duration of congestion that result from changes in demand or capacity. This limitation is overcome in the Vickrey/Arnott, de Palma Lindsey bottleneck model, which combines congestion in the form of queuing behind a bottleneck with users' trip-timing preferences and departure time decisions. We derive the user equilibrium and social optimum for the basic bottleneck model, and explain how the optimum can be decentralized using a time-varying toll. They then review some extensions of the basic model that encompass elastic demand, user heterogeneity, stochastic demand and capacity and small networks. We conclude by identifying some unresolved modelling issues that apply not only to the bottleneck model but to trip-timing preferences and congestion dynamics in general

Congestion pricing, infrastructure investment and redistribution

We study congestion pricing by a government that has redistributive concerns, in the presence of optimal income taxation. Individuals differ in (unobservable) earning ability and consumption technology for commodities using a congestible network (e.g. roads, Internet). We find, assuming separable preferences, that when efficiency of consumption technology is either invariant or postively correlated with earning ability, low ability individuals should face higher marginal congestion charges than high ability ones. Moreover, reducing congestion (by raising charges or expanding network capacity) enables government to increase redistribution. We also find that means tested congestion pricing may be necessary to implement the second-best allocation.congestion pricing; income taxation; redistribution; infrastructure investment