A Multiclass Cumulative Prospect Theory-Based Stochastic User Equilibrium Model with Path Constraints in Degradable Transport Networks

The limited driving range and the unavailability or insufficiency of battery charging/swapping stations cause the so-called range anxiety issue for traffic assignment involving battery electric vehicle (BEV) users. In addition, expected utility theory-based stochastic user equilibrium (EUT-SUE) model generates the perfectly rational issue when the travellers make route choice decisions. To tackle these two problems, this article improves the cumulative prospect theory-based stochastic user equilibrium (CPT-SUE) model in a degradable transport network through incorporating the constraints of multiple user classes and distance limit. In this degradable network, the travellers experience stochastic travel times due to network link capacity degradations. For this improved CPT-SUE model, the equivalent variational inequality (VI) model and associated method of successive averages (MSA) based solution are provided. The improved CPT-SUE model is tested and compared with the EUT-SUE model with distance limit, with results showing that the improved CPT-SUE model can handle jointly the range anxiety issue and the perfectly rational issue

주행 중 충전 행태를 고려한 배터리 전기차의 동적 이용자 평형 모형

학위논문 (석사)-- 서울대학교 대학원 : 건설환경공학부, 2017. 2. 고승영.Facing with increasing demands on Battery Electric Vehicle (BEV), public interests of incorporating BEVs into existing operational and planning models of transportation systems are growing recently. Unlike gasoline vehicles, BEV users encounter range anxiety that comes from short driving range, long charging time, and insufficient charging infrastructures. BEV drivers who encounters the range anxiety problem are expected to have different route choice behaviors from the existing drivers and will resist being stuck in the middle of trips, for not having enough battery states. Several models have been developed to reflect this aspect in static traffic assignment models. However, Dynamic Traffic Assignment (DTA) incorporating BEVs on it have hardly been researched yet. In addition to generally researched static models, the temporal approach toward BEV can broaden analytical scopes in a temporal manner and can be used for analysis related to operational planning. Therefore, this research proposes a Dynamic User Equilibrium (DUE) model of BEVs that can reflect drivers behaviors incurred by range anxiety. In this research, a trip-based DTA model for BEVs is developed. It is because the usable paths of a BEV are completely determined by its remaining battery state, a path-based discrete time DTA model is formulated to track the battery states of BEVs at each time interval. The models objective function is composed of travel time and out-of-range penalty term induced from battery shortage for assigned path flows. A modified iterative flow swapping algorithm is adopted to gradually decrease the out-of-range penalty and travel time gap between shortest paths and non-shortest paths. The suggested model is applied to an example problem of Nguyen-Dupuis network with insufficient initial battery state. As a result, given with insufficient initial battery state, traffic flows of BEV detoured to maintain their batteries to be higher than minimal comfortable amount in a dynamic transportation system. The experienced travel time for traffic flows that had the same O-D and departing time interval showed identical value and traversed their paths without running out of batteries.1.Introduction 1 1.1.Background 1 1.2. Motivation 2 1.3. Objectives 5 1.4. Organization of Thesis 7 2.Literature Review 8 2.1. Static User Equilibrium Models 8 2.2. Dynamic User Equilibrium Models 13 2.3. Review result 16 3.Model Development 18 3.1.Problem Description 18 3.2.Mathematical Formulation 19 4.Algorithm 30 4.1.Flow Swapping algorithm 30 4.2. Modified algorithm 31 5.Numerical Example 35 5.1.Network properties 35 5.2.Assignment result 37 6.Conclusions 43 6.1.Summary and conclusion 43 국문 초록 49Maste

Interdependence between transportation system and power distribution system: a comprehensive review on models and applications

The rapidly increasing penetration of electric vehicles in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment protection. Integrating charging facilities, especially high-power chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced, especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is highlighted. Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.fi=vertaisarvioitu|en=peerReviewed

TOOLS TO SUPPORT TRANSPORTATION EMISSIONS REDUCTION EFFORTS: A MULTIFACETED APPROACH

The transportation sector is a significant contributor to current global climatic problems, one of the most prominent problems that today's society faces. In this dissertation, three complementary problems are addressed to support emissions reduction efforts by providing tools to help reduce demand for fossil fuels. The first problem addresses alternative fuel vehicle (AFV) fleet operations considering limited infrastructure availability and vehicle characteristics that contribute to emission reduction efforts by: supporting alternative fuel use and reducing carbon-intensive freight activity. A Green Vehicle Routing Problem (G-VRP) is formulated and techniques are proposed for its solution. These techniques will aid organizations with AFV fleets in overcoming difficulties that exist as a result of limited refueling infrastructure and will allow companies considering conversion to a fleet of AFVs to understand the potential impact of their decision on daily operations and costs. The second problem is aimed at supporting SOV commute trip reduction efforts through alternative transportation options. This problem contributes to emission reduction efforts by supporting reduction of carbon-intensive travel activity. Following a descriptive analysis of commuter survey data obtained from the University of Maryland, College Park campus, ordered-response models were developed to investigate the market for vanpooling. The model results show that demand for vanpooling in the role of passenger and driver have differences and the factors affecting these demands are not necessarily the same. Factors considered include: status, willingness-to-pay, distance, residential location, commuting habits, demographics and service characteristics. The third problem focuses on providing essential input data, origin-destination (OD) demand, for analysis of various strategies, to address emission reduction by helping to improve system efficiency and reducing carbon-intensive travel activity. A two-stage subarea OD demand estimation procedure is proposed to construct and update important time-dependent OD demand input for subarea analysis in an effort to overcome the computational limits of Dynamic Traffic Assignment (DTA) methodologies. The proposed method in conjunction with path-based simulation-assignment systems can provide an evolving platform for integrating operational considerations in planning models for effective decision support for agencies that are considering strategies for transportation emissions reduction

Bridging the user equilibrium and the system optimum in static traffic assignment: a review

Solving the road congestion problem is one of the most pressing issues in modern cities since it causes time wasting, pollution, higher industrial costs and huge road maintenance costs. Advances in ITS technologies and the advent of autonomous vehicles are changing mobility dramatically. They enable the implementation of a coordination mechanism, called coordinated traffic assignment, among the sat-nav devices aiming at assigning paths to drivers to eliminate congestion and to reduce the total travel time in traffic networks. Among possible congestion avoidance methods, coordinated traffic assignment is a valuable choice since it does not involve huge investments to expand the road network. Traffic assignments are traditionally devoted to two main perspectives on which the well-known Wardropian principles are inspired: the user equilibrium and the system optimum. User equilibrium is a user-driven traffic assignment in which each user chooses the most convenient path selfishly. It guarantees that fairness among users is respected since, when the equilibrium is reached, all users sharing the same origin and destination will experience the same travel time. The main drawback in a user equilibrium is that the system total travel time is not minimized and, hence, the so-called Price of Anarchy is paid. On the other hand, the system optimum is an efficient system-wide traffic assignment in which drivers are routed on the network in such a way the total travel time is minimized, but users might experience travel times that are higher than the other users travelling from the same origin to the same destination, affecting the compliance. Thus, drawbacks in implementing one of the two assignments can be overcome by hybridizing the two approaches, aiming at bridging users’ fairness to system-wide efficiency. In the last decades, a significant number of attempts have been done to bridge fairness among users and system efficiency in traffic assignments. The survey reviews the state-of-the-art of these trade-off approaches

Building a large-scale micro-simulation transport scenario using big data

A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources

Dynamic Modeling and Real-time Management of a System of EV Fast-charging Stations

Demand for electric vehicles (EVs), and thus EV charging, has steadily increased over the last decade. However, there is limited fast-charging infrastructure in most parts of the world to support EV travel, especially long-distance trips. The goal of this study is to develop a stochastic dynamic simulation modeling framework of a regional system of EV fast-charging stations for real-time management and strategic planning (i.e., capacity allocation) purposes. To model EV user behavior, specifically fast-charging station choices, the framework incorporates a multinomial logit station choice model that considers charging prices, expected wait times, and detour distances. To capture the dynamics of supply and demand at each fast-charging station, the framework incorporates a multi-server queueing model in the simulation. The study assumes that multiple fast-charging stations are managed by a single entity and that the demand for these stations are interrelated. To manage the system of stations, the study proposes and tests dynamic demand-responsive price adjustment (DDRPA) schemes based on station queue lengths. The study applies the modeling framework to a system of EV fast-charging stations in Southern California. The results indicate that DDRPA strategies are an effective mechanism to balance charging demand across fast-charging stations. Specifically, compared to the no DDRPA scheme case, the quadratic DDRPA scheme reduces average wait time by 26%, increases charging station revenue (and user costs) by 5.8%, while, most importantly, increasing social welfare by 2.7% in the base scenario. Moreover, the study also illustrates that the modeling framework can evaluate the allocation of EV fast-charging station capacity, to identify stations that require additional chargers and areas that would benefit from additional fast-charging stations

A user equilibrium-based fast-charging location model considering heterogeneous vehicles in urban networks

Inappropriate deployment of charging stations not only hinders the mass adoption of Electric Vehicles (EVs) but also increases the total system costs. This paper attempts to address the problem of identifying the optimal locations of fast-charging stations in the urban network of mixed gasoline and electric vehicles with respect to the traffic equilibrium flows and the EVs' penetration. A bi-level optimization framework is proposed in which the upper level aims to locate charging stations by minimizing the total travel time and the installation costs for charging infrastructures. On the other hand, the lower-level captures re-routing behaviours of travellers with their driving ranges. A cross-entropy approach is developed to deliver the solutions with different levels of EVs' penetration. Finally, numerical studies are performed to demonstrate the fast convergence of the proposed framework and provide insights into the impact of EVs' proportion in the network and the optimal location solution on the global system cost