91 research outputs found
Handling multiple objectives in optimization of externalities as objectives for dynamic traffic management.
Dynamic traffic management (DTM) is acknowledged in various policy documents as an important instrument to improve network performance. This network performance is not only a matter of accessibility, since the externalities of traffic are becoming more and more important objectives as well. Optimization of network performance using DTM measures is a specific example of a network design problem (NDP) and incorporation of externality objectives results in a multi objective network design problem (MO NDP)). Solving this problem resorts in a Pareto optimal set of solutions. A framework is presented with the non-dominated sorting algorithm (NSGAII), the Streamline dynamic traffic assignment model and several externality models, that is used to solve this MO NDP. With a numerical experiment it is shown that the Pareto optimal set provides important information for the decision making process, which would not have been available if the optimization problem was simplified by incorporation of a compensation principle in advance. However, in the end a solution has to be chosen as the best compromise. Since the Pareto optimal set can be difficult to comprehend, ranking it may be necessary to assist the decision makers. Cost benefit analysis which uses the economic compensation principle is a method that is often used for ranking the alternatives. This research shows, that travel time costs are by far the most dominant objective. Therefore other ranking methods should be considered. Differences between these methods are explained and it is illustrated that the outcomes and therefore the eventual decisions taken can be different
Controlling user groups in traffic
On the basis of policy-based target groups, we developed a prioritization strategy for traffic streams and applied it with the adaptive urban traffic control (UTC) ImFlow. Our main aim was to gain understanding of the possibilities of a policy driven prioritization in an urban context. We conclude that traffic light control can become more rational, effective and efficient from a policy viewpoint. However, situational and operational constraints pose a limit
Quasi-dynamic network loading: Adding queuing and spillback to static traffic assignment
For many years, static traffic assignment models have been widely applied in transport planning studies and will continue to be an important tool for strategic policy decisions. As is well known, in the traditional approach, the location of the delays and queues are not predicted correctly, and the resulting travel times do not correspond well with reality. Dynamic models can approach reality much better, but come at a computational cost. In this paper we propose a quasi-dynamic model which inherits most of the computational efficiency of static models, but aims to keep most of the important dynamic features, such as queuing, spillback, and shockwaves. Instead of adjusting the traditional static model or using heuristics, we theoretically derive the model from the dynamic link transmission model, assuming stationary travel demand and instantaneous flow. Furthermore, we present algorithms for solving the model. On a corridor network we illustrate the feasibility and compare it with other approaches, and on a larger network of Amsterdam we discuss the computational efficiency
Genetics of traffic assignment models for strategic transport planning
This paper presents a review and classification of traffic assignment models for strategic transport planning purposes by using concepts analogous to genetics in biology. Traffic assignment models share the same theoretical framework (DNA), but differ in functionality (genes). We argue that all traffic assignment models can be described by two genes. The first gene determines the spatial functionality (unrestricted, capacity restrained, capacity constrained, capacity and storage constrained) described by five spatial interaction assumptions, while the second gene determines the temporal functionality (static, semi-dynamic, dynamic) described by two temporal interaction assumptions. This classification provides a deeper understanding of the often implicit assumptions made in traffic assignment models described in the literature, particularly with respect to networking loading where the largest differences occur. It further allows for comparing different models in terms of functionality, and opens the way for developing novel traffic assignment models
Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques
Public road authorities and private mobility service providers need
information derived from the current and predicted traffic states to act upon
the daily urban system and its spatial and temporal dynamics. In this research,
a real-time parking area state (occupancy, in- and outflux) prediction model
(up to 60 minutes ahead) has been developed using publicly available historic
and real time data sources. Based on a case study in a real-life scenario in
the city of Arnhem, a Neural Network-based approach outperforms a Random
Forest-based one on all assessed performance measures, although the differences
are small. Both are outperforming a naive seasonal random walk model. Although
the performance degrades with increasing prediction horizon, the model shows a
performance gain of over 150% at a prediction horizon of 60 minutes compared
with the naive model. Furthermore, it is shown that predicting the in- and
outflux is a far more difficult task (i.e. performance gains of 30%) which
needs more training data, not based exclusively on occupancy rate. However, the
performance of predicting in- and outflux is less sensitive to the prediction
horizon. In addition, it is shown that real-time information of current
occupancy rate is the independent variable with the highest contribution to the
performance, although time, traffic flow and weather variables also deliver a
significant contribution. During real-time deployment, the model performs three
times better than the naive model on average. As a result, it can provide
valuable information for proactive traffic management as well as mobility
service providers.Comment: Proc. of Transportation Research Board 2020 Annual Meeting,
Washington D.C., USA, January 202
Improving Operational Efficiency In EV Ridepooling Fleets By Predictive Exploitation of Idle Times
In ridepooling systems with electric fleets, charging is a complex decision-making process. Most electric vehicle (EV) taxi services require drivers to make egoistic decisions, leading to decentralized ad-hoc charging strategies. The current state of the mobility system is often lacking or not shared between vehicles, making it impossible to make a system-optimal decision. Most existing approaches do not combine time, location and duration into a comprehensive control algorithm or are unsuitable for real-time operation. We therefore present a real-time predictive charging method for ridepooling services with a single operator, called Idle Time Exploitation (ITX), which predicts the periods where vehicles are idle and exploits these periods to harvest energy. It relies on Graph Convolutional Networks and a linear assignment algorithm to devise an optimal pairing of vehicles and charging stations, in pursuance of maximizing the exploited idle time. We evaluated our approach through extensive simulation studies on real-world datasets from New York City. The results demonstrate that ITX outperforms all baseline methods by at least 5% (equivalent to $70,000 for a 6,000 vehicle operation) per week in terms of a monetary reward function which was modeled to replicate the profitability of a real-world ridepooling system. Moreover, ITX can reduce delays by at least 4.68% in comparison with baseline methods and generally increase passenger comfort by facilitating a better spread of customers across the fleet. Our results also demonstrate that ITX enables vehicles to harvest energy during the day, stabilizing battery levels and increasing resilience to unexpected surges in demand. Lastly, compared to the best-performing baseline strategy, peak loads are reduced by 17.39% which benefits grid operators and paves the way for more sustainable use of the electrical grid
- …