Melissa Duell

Effect of Road Grade on Networkwide Vehicle Energy Consumption and Ecorouting

Because of growing concern about the impact of emissions from the transport sector on global climate change, vehicle energy consumption is a factor of great interest to network planners. In addition, drivers are interested in reducing energy consumption and, thus, fuel costs. However, traditional models of vehicle energy consumption have neglected an important factor: road grade. This assumption has traditionally been supported by the idea that the energy consumed because of the road grade would be reflected in changes in speed and acceleration, but a demonstration of this on an aggregate network in a city of a realistic size has been difficult to show. This work demonstrated the impact of road grade on networkwide vehicle energy consumption by the integration of energy consumption equations based on road load equations, elevation data available from the Google Elevation advanced programming interface, and a dynamic traffic assignment model to capture the effect of user route choice. This work quantified the impact of the energy consumed because of road grades on two city networks, and the results indicate that the effects of grades should not be excluded from evaluations of vehicle energy consumption. In addition, the effects of eco-routing, in which drivers choose the shortest path that consumes the least amount of energy, were explored. The results for the city networks indicate that if drivers do not account for grades, they might choose a route that actually increases vehicle energy consumption. The proposed modeling tool is scalable and easily adaptable to different cities.

Evaluation of a Strategic Road Pricing Scheme Accounting for Day-to-Day and Long-Term Demand Uncertainty

Transport network pricing schemes are an integral traffic management strategy that can be implemented to reduce congestion, among other network impacts. However, the problem of determining tolls becomes much more complex when multiple sources of demand uncertainty are considered. This paper proposes a novel tolling model based on a particular variant of strategic user equilibrium in which users base their route choice decisions on a known demand distribution. The study showed that by using an average daily demand, a marginal social cost–based tolling approach could induce near optimal conditions in a strategic network. However, uncertainty was associated with the long-term future planning demand; inaccurate forecasts of future demand could result in poor realized tolling scheme performance. Therefore, this paper also proposes a method to test the robustness of a tolling scheme, which is the reliability of the link tolls under a range of future demand scenario realizations. Results demonstrated that evaluations of strategic tolling schemes differed when both the short-term and the long-term uncertainty in demand were accounted for, and furthermore suggested that future research into the integration of multiple sources of uncertainty into the pricing scheme evaluation is merited.

Impact of Autonomous Vehicles on Traffic Management: Case of Dynamic Lane Reversal

As the future of autonomous vehicles (AVs) becomes more certain, transport network managers may seek ways to reinvent elements of the traffic network to improve efficiency. One possibility is dynamic lane reversal, in which the network operator makes use of AV communications and behavior to change the direction of flow on a road link at smaller time intervals than would be possible with human drivers. Although there is much research into the mechanical details of AVs, this study motivates the need for future research by focusing on a planning application in which AVs are already present. A novel extension to an established system optimal dynamic traffic assignment model based on the cell transmission model was examined. The model determined the optimal lane configuration at small space–time intervals. Results demonstrate the model on a single link and a grid network and explore the dynamic demand scenarios that are most conducive to increasing system efficiency with dynamic lane reversal.

Deployment and Calibration Considerations for Large-Scale Regional Dynamic Traffic Assignment: Case Study for Sydney, Australia

Dynamic traffic assignment (DTA) has received increasing attention in recent years, and there are numerous examples of practical implementations. This work adds to the literature by describing the ongoing experience of building the first large-scale simulation-based DTA model in Australia. The input data for the model are summarized, and an in-depth discussion and an analysis of model output and the calibration process are presented. Current results put 80% of the 322 calibration points spread across the network within an acceptable bound of error, but the project found that alternative metrics of network performance also must be considered so that other aspects of model realism are not neglected. The described DTA model could be used for evaluating important policy decisions and infrastructural development in the context of the macro- and mesoscale network operation. Additionally, this project is a proof of concept for the Australian region and may provide insight to practitioners interested in emerging areas of transport planning and traffic modeling.

Implications of Volatility in Day-to-Day Travel Flow and Road Capacity on Traffic Network Design Projects

This work addresses the traffic network design problem when day-to-day uncertainties in travel demand and link capacity are taken into account. Specifically, this work proposes a network design formulation that uses a strategic behavior approach in which total demand and link capacity are treated as random variables, and a strategic user equilibrium results in fixed equilibrium link proportions. The bilevel model is formulated, system performance metrics are derived, and a solution method is then developed according to a tailored genetic algorithm. Results under varying levels of volatility reflect possible suboptimal project selection when a deterministic modeling approach is used.

How Should Travel Demand and Supply Models Be Jointly Calibrated

Calibration is a critical aspect of model development that has long been recognized by researchers as a challenging issue. In particular, difficulties arise when the observed data used for calibration do not match the model output, which is the case in the majority of transport planning models. In the traditional calibration process, the origin–destination (OD) matrices are the key interface between demand and supply models, which could lead to issues when observed traffic link counts are used to update the OD matrix, causing a loss of key demand characteristics in the process. Developing a unified structure for modeling both demand and supply requires a calibration process that meets the requirements of both types of models, a serious issue which has received less attention in the literature. In this paper, the existing processes of developing and integrating demand and supply models are discussed and then examined using a case study in the Melbourne area. The numerical results show that the standard OD calibration procedure causes unrealistic changes in the OD matrix. Finally, some possible solutions to address the current limitations in development of a unified structure are discussed.

Integrating uncertainty considerations into multi-objective transportation network design projects accounting for environment disruption

Abstract Few previous works integrated both uncertainty and environment disruption into traffic network design problems (NDPs). This study aims to address this gap. First, the mathematical framework of the strategic user equilibrium (StrUE) traffic assignment under volatility of both total travel demand and link capacity is analyzed. Second, we incorporate the StrUE traffic assignment into a network design project and propose a multi-objective bi-level NDP program. Two objective functions are formulated, which are respectively to minimize the expected total system travel time and minimize the expected total system off-gas emissions under StrUE. Third, we develop two tailored solution methods – an enumerative algorithm and a metaheuristic method based on a genetic algorithm. Finally, systematic evaluation of the performance of the proposed approach is conducted. The results highlight that ignoring uncertainty considerations can result in sub-optimal design solutions in terms of expected network performance. Also, the two objectives, to minimize system-level travel time and vehicle emission, are conflicting for certain design scenarios.

System Optimal Dynamic Lane Reversal for Autonomous Vehicles

Transformative technologies such as autonomous vehicles (AVs) create an opportunity to reinvent features of the traffic network to improve efficiency. The focus of this work is dynamic lane reversal: using AV communications and behavior to change the direction vehicles are allowed to travel on a road lane with much greater frequency than would be possible with human drivers. This work presents a novel methodology based on the linear programming formulation of dynamic traffic assignment using the cell transmission model for solving the system optimal (SO) problem. The SO assignment is chosen because the communications and behavior protocols necessary to operate AV intersection and lane reversal controls could be used to assign routes and optimize network performance. This work expands the model to determine the optimal direction of lanes at small space-time intervals. Model assumptions are outlined and discussed. Results demonstrate the model and explore the dynamic demand scenarios which are most conducive to increasing system efficiency with dynamic lane reversal.

The System Impact of Travel Demand Variability in the Context of Electric Vehicles

The introduction of plug-in electric vehicles (PEVs) represents an unprecedented interaction between the road network and electricity grid. In this new integrated system, travel demand, behavior, and traffic congestion will influence the temporal and spatial characteristics of electricity usage and environmental impacts. Furthermore, uncertainty in the transport characteristics will manifest as a new uncertainty placed on electrical infrastructure. Overall, the realized system-level impacts depend on the eventual penetration of PEV ownership. However the true market share of PEVs in the future is highly unclear and radically different scenarios are possible. This added forecasting volatility makes long-term transport models that explicitly consider travel demand uncertainty even more critical. This work utilizes transport modeling tools in order to quantify the relationship between the travel patterns of PEV drivers and PEV energy consumption rates. Furthermore, this research explicitly addresses the relationship between long term travel demand uncertainty and system level energy consumption variability, an essential issue for regional energy providers and planners. Implications are demonstrated on the Sioux Falls network.

Strategic dynamic traffic assignment incorporating travel demand uncertainty

ABSTRACT Dynamic traffic assignment (DTA) research has advanced greatly in terms of deployability, computational feasibility, and representing complex temporal phenomena. There have also been substantial contributions regarding various aspects of stochasticity within DTA. However, there are persisting limitations in terms of approaches which are both computationally tractable and provide more detailed representation of stochastic aspects. This paper explores the application of a novel Strategic User Equilibrium DTA (StrDTA) modelling framework, which captures the impact of users making a priori route choice decisions based on the knowledge of a range of possible travel demand scenarios (e.g. differing days or representative situations). The resulting stochastic DTA problem becomes complex due to the integration of multiple demand scenarios and the algorithmic adjustments necessary to find optimal paths. A new solution framework is proposed which still permits implementation, and a detailed case study is presented for the Sydney Central Business District network. Results demonstrate the importance of accounting for stochasticity in the routing algorithm rather than relying on assumptions of average values.