David Z.W. Wang

V2G Capacity Estimation Using Dynamic EV Scheduling

An aggregated number of electric vehicles (EVs) provides a platform for smart energy storage (SES) in buildings which can be used during periods of maximum demand or high electricity price as well as for leveling the intermittent outputs of renewable energy sources (RESs). However, the vehicle to grid (V2G) capacity varies over time based on the availability of the EVs and their individual state of charge (SOC). Hence a real-time V2G capacity estimation is important for utilizing EVs as SES efficiently. In this paper, an algorithm for half-hourly V2G capacity estimation using real-time EV scheduling is proposed. The algorithm is implemented as part of the Building Energy Management System (BEMS). The BEMS uses forecasted building load demand without EVs and predicted charging profiles of the EVs connected to the building for estimating the V2G capacity. The estimated V2G capacity and the availability of RESs are considered by the BEMS to schedule the EV charging/discharging. The proposed algorithm is applied to study three case scenarios using BEMS on residential, office, and commercial buildings in Singapore. The results obtained clearly show that a group of EVs connected to any high-rise building can be effectively used as a distributed storage system.

Train routing model and algorithm combined with train scheduling.

This paper constructs a train routing model combined with a train scheduling problem, which is a 0–1 mixed-integer nonlinear programming problem. Except for train route choice, the model considers a system of complicated constraints on headway, trip time, meeting-crossing and overtaking between trains, capacity of siding, and so on. Based on the delay information of each train, a route adjustment algorithm is designed to obtain satisfactory route schemes of trains. Moreover, a tabu search procedure is designed to further improve the route schemes. The simulation results show that, relative to the optimal solution, the solutions obtained by the current method exhibit small relative error. The tabu search algorithm exhibits unstable performance because of dependence on the initial solution. Combined with the route adjust algorithm, the tabu search technique can improve the quality and stability of solutions. In addition, the departure order of heterogeneous trains exerts important influences on train route choice.

Modelling urban traffic dynamics based upon the variational formulation of kinematic waves

This paper presents a dynamic traffic modelling framework based on the variational formulation of kinematic waves. We compare the effectiveness of this relatively recent numerical method with the traditional Godunov-based cell transmission method on various aspects including modelling shocks, dispersion of vehicle platoons, moving bottlenecks, and traffic characteristics with respect to real-world observations made in Central London, UK. The results suggest that the variational method is able to produce high-quality estimates both theoretically and empirically. This study opens up a new research direction in the area of urban traffic modelling and optimisation.

Identification of critical combination of vulnerable links in transportation networks – a global optimisation approach

This paper presents a global optimisation framework for identifying the most critical combination of vulnerable links in a transportation network. The problem is formulated as a mixed-integer non-linear programme with equilibrium constraints, aiming to determine the combination of links whose deterioration would induce the most increase in total travel cost in the network. A global optimisation solution method applying a piecewise linearisation approach and range-reduction technique is developed to solve the model. From the numerical results, it is interesting and counterintuitive to note that the set of most vulnerable links when simultaneous multiple-link failure occurs is not simply the combination of the most vulnerable links with single-link failure, and the links in the critical combination of vulnerable links are not necessarily connected or even in the neighbourhood of each other. The numerical results also show that the ranking of vulnerable links will be significantly affected by certain input parameters.

Electric vehicle charging profile prediction for efficient energy management in buildings

Predicting the charging profiles of electric vehicles (EVs) connected to a building incorporated with a Building Energy Management System (BEMS) will improve the energy efficiency of the building. The predicted charging profiles along with the forecasted load data can be utilized for calculating vehicle to grid (V2G) capacity and for performing load/source scheduling. In this paper, an Artificial Neural Network (ANN) based model is proposed for predicting the charging profiles of EVs connected to a building. The ANN model considers the previous charging profiles, initial State of Charge (SOC) and final SOC for predicting the charging profile of the EV. A BEMS simulation tool is developed using National Instruments LabVIEW to analyze the functionality of the model. Using the predicted charging profiles and forecasted building load, EV scheduling is demonstrated for a typical day. The V2G capacity available for peak saving is also computed and load/source scheduling is performed accordingly.

Tree‐Based Logistic Regression Approach for Work Zone Casualty Risk Assessment

This study presents a tree-based logistic regression approach to assessing work zone casualty risk, which is defined as the probability of a vehicle occupant being killed or injured in a work zone crash. First, a decision tree approach is employed to determine the tree structure and interacting factors. Based on the Michigan M-94\I-94\I-94BL\I-94BR highway work zone crash data, an optimal tree comprising four leaf nodes is first determined and the interacting factors are found to be airbag, occupant identity (i.e., driver, passenger), and gender. The data are then split into four groups according to the tree structure. Finally, the logistic regression analysis is separately conducted for each group. The results show that the proposed approach outperforms the pure decision tree model because the former has the capability of examining the marginal effects of risk factors. Compared with the pure logistic regression method, the proposed approach avoids the variable interaction effects so that it significantly improves the prediction accuracy.

Macroscopic analysis of the fundamental diagram with inhomogeneous network and instable traffic

In this study, a region-based dynamic model is proposed to depict the traffic dynamics of an idealised grid-like unidirectional road network, and the impact of heterogeneity of the network on macroscopic traffic flow is investigated. The equilibrium solutions are obtained by solving the dynamic model, and the method of linear stability analysis is applied to test the stability of the solutions. Accordingly, the curves of macroscopic fundamental diagram (MFD) for various inhomogeneous networks are plotted and the effects of network heterogeneity on MFD are revealed. Two network heterogeneity parameters, road capacity and the attraction strength of a sub-region in the road network, are considered and the results show that both of them affect the shape of MFD significantly. Numerical simulations are performed and the result supports the theoretical analysis.

Equilibrium trip scheduling in single bottleneck traffic flows considering multi-class travellers and uncertainty – a complementarity formulation

In this paper, we consider the equilibrium trip scheduling in single bottleneck traffic flows under uncertainty with multi-class travellers. Travel time uncertainty is considered explicitly in the scheduling model, and heterogeneity parameters are assumed to describe the multi-class users. We will present a model applying nonlinear complementarity system approach to formulate and solve the equilibrium trip scheduling problem with multi-class commuters considering travel time uncertainty. The paper presents theoretical proofs for solution existence, and numerical results are conducted to show the model validity.

Effects of traffic lights for Manhattan-like urban traffic network in intelligent transportation systems

ABSTRACT Traffic light is the core part of advanced transportation management systems. Assuming travelers receive and follow the route guidance information designed by two specific route choice strategies, this paper investigates how the traffic lights rule, period and its quantity affect the traffic system performance on a Manhattan-like urban network. Firstly, the simulation results of the average flow against the traffic density and the vehicle distribution are studied under four traffic light rules. Then the relationship between the extremum of average speed and traffic light period is complementally analyzed and the theoretical results have been proved basically in agreement with the simulation results. Lastly, the effects of the number of traffic lights on average flow and vehicle distribution are discussed. From these results, it is concluded that the traffic system performance can be improved if the anticlockwise rule combined with the congestion coefficient feedback strategy-based route guidance is adopted and the number of traffic lights is reduced to its minimum requirement.

Taxi Dispatching and Stable Marriage

This study explores the challenge of the dynamic dispatching of taxis to the immediate passenger booking requests. In particular, the study leverages on a stable marriage assignment algorithm and applies it for dispatching taxis to passengers. The stable marriage algorithm was developed initially for matching men and women according to their preferences in polynomial time. The results of the custom built simulation model show that the taxi dispatching strategy based on the stable marriage matching improves the taxi operation performance in all observed indicators (taxi profit, number of served passengers, not-occupied and total taxi mileage and passenger waiting time) as compared to the standard first-come, first-served strategy.