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

A unified framework for traffic assignment: deriving static and quasi‐dynamic models consistent with general first order dynamic traffic assignment models

This paper presents a theoretical framework to derive static, quasi-dynamic, and semi-dynamic traffic assignment models from a general first order dynamic traffic assignment model. By explicit derivation from a dynamic model, the resulting models maintain maximum consistency with dynamic models. Further, the derivations can be done with any fundamental diagram, any turn flow restrictions, and deterministic or stochastic route choice. We demonstrate the framework by deriving static (quasidynamic) models that explicitly take queuing and spillback into account. These models are generalisations of models previously proposed in the literature. We further discuss all assumptions usually implicitly made in the traditional static traffic assignment model

Traffic state prediction services for automated driving and traffic management.

The target in the PRYSTINE project is to realize Fail-operational Urban Surround perceptION (FUSION), which is based on sensor fusion, and control functions in order to enable safe automated driving in urban and rural environments. Estimation of the complete current and (near) future traffic conditions ahead, beyond the range of on-board vehicle sensors, provides the automated driving controller with enhanced information to act better and more comfortably in the current situation and to extent road safety. Traffic state prediction is also an important input for pro-active traffic management as identified within TM2.0 (Traffic Management2.0 vision ERTICO). The derivation of a common operational picture for traffic management and mobility service providers, like CAV, enables the collaboration between public and private parties in facilitating traffic. Stimulating and enhancing this collaboration is part of the Dutch innovation program MobilitymoveZ. Significant improvements of quality and availability of data offers the opportunity to provide such information. By combining data science and traffic modelling techniques, an application is developed consisting of current and short term traffic prediction (typically up to 10 minutes ahead) and a virtual patrol detecting congestion and incidents for urban and non-urban networks

Comparison of evolutionary multi objective algorithms for the dynamic network design problem

In traffic and transport a significant portion of research and application is focused on single objective optimization, although there is rarely only one objective that is of interest. The externalities of traffic are of increasing importance for policy decisions related to the design of a road network. The optimization of externalities using dynamic traffic management measures is a multi objective network design problem. The presence of multiple conflicting objectives makes the optimization problem challenging to solve. Evolutionary multi objective algorithms has been proven successful in solving multi objective optimization problems. However, like all optimization methods, these are subject to the free lunch theorem. Therefore, we compare the NSGAII, SPEA2 and SPEA2+ algorithms in order to find a Pareto optimal solution set for this optimization problem. Because of CPU time limitation as a result of solving the lower level using a dynamic traffic assignment model, the performance by the algorithms is compared within a certain budget. The externalities optimized are noise, climate and accessibility. In a numerical experiment the SPEA2+ outperforms the SPEA2 on all used measures. Comparing NSGAII and SPEA2+, there is no clear evidence of one approach outperforming the other

Accelerating solving the dynamic multi-objective nework design problem using response surface methods

Multi objective optimization of externalities of traffic solving a network design problem in which Dynamic Traffic Management measures are used, is time consuming while heuristics are needed and solving the lower level requires solving the dynamic user equilibrium problem. Use of response surface methods in combination with evolutionary algorithms could accelerate the determination of the Pareto optimal set. Three of these methods are compared with employing the SPEA2+ evolutionary algorithm without use of these methods. The results show that the RSM methods accelerate the search considerably at the start, but tend to converge faster and therefore loose their head start

The multi-objective network design problem using minimizing externalities as objectives: comparison of a genetic algorithm and simulated annealing framework.

Incorporation of externalities in the Multi-Objective Network Design Problem (MO NDP) as objectives is an important step in designing sustainable networks. In this research the problem is defined as a bi-level optimization problem in which minimizing externalities are the objectives and link types which are associated with certain link characteristics are the discrete decision variables. Two distinct solution approaches for this multi-objective optimization problem are compared. The first heuristic is the non-dominated sorting genetic algorithm II (NSGA-II) and the second heuristic is the dominance based multi objective simulated annealing (DBMO-SA). Both heuristics have been applied on a small hypothetical test network as well as a realistic case of the city of Almelo in the Netherlands. The results show that both heuristics are capable of solving the MO NDP. However, the NSGA-II outperforms DBMO-SA, because it is more efficient in finding more non-dominated optimal solutions within the same computation time and maximum number of assessed solutions

A unified framework for traffic assignment: deriving static and quasi-dynamic models consistent with general first order dynamic traffic assignment models

This paper presents a theoretical framework to derive static, quasi-dynamic, and semi-dynamic traffic assignment models from a general first order dynamic traffic assignment model. By explicit derivation from a dynamic model, the resulting models maintain maximum consistency with dynamic models. Further, the derivations can be done with any fundamental diagram, any turn flow restrictions, and deterministic or stochastic route choice. We demonstrate the framework by deriving static (quasidynamic) models that explicitly take queuing and spillback into account. These models are generalisations of models previously proposed in the literature. We further discuss all assumptions usually implicitly made in the traditional static traffic assignment model

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 on and 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 out-flux) 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 Forrest-based one on all assessed performance measures, although the differences are small. Both are outperforming a naïve, seasonal random walk model. Although the performance degrades with increasing the prediction horizon, the model shows a performance gain of over 150% at a prediction horizon of 60 minutes compared with the naïve model. Furthermore, it is shown that predicting the in- and out-flux 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 out-flux is less sensitive for 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 3 times better than the naïve model on average. As a result, it can provide valuable information for proactive traffic management as well as mobility service providers