A Method for the Optimization of Daily Activity Chains Including Electric Vehicles

Abstract

The focus of this article is to introduce a method for the optimization of daily activity chains of travelers who use Electric Vehicles (EVs) in an urban environment. An approach has been developed based on activity-based modeling and the Genetic Algorithm (GA) framework to calculate a suitable schedule of activities, taking into account the locations of activities, modes of transport, and the time of attendance to each activity. The priorities of the travelers concerning the spatial and temporal flexibility were considered, as well as the constraints that are related to the limited range of the EVs, the availability of Charging Stations (CS), and the elevation of the road network. In order to model real travel behavior, two charging scenarios were realized. In the first case, the traveler stays in the EV at the CS, and in the second case, the traveler leaves the EV to charge at the CS while conducting another activity at a nearby location. Through a series of tests on synthetic activity chain data, we proved the suitability of the method elaborated for addressing the needs of travelers and being utilized as an optimization method for a modern Intelligent Transportation System (ITS).

Simulation of the Daily Activity Plans of Travelers Using the Park-and-Ride System and Autonomous Vehicles: Work and Shopping Trip Purposes

“…Finally, maximization of the utility of people, when they conduct their daily activities, is needed, and this can be obtained by using the most suitable transport mode, best route, and a suitable travel time and cost. The vehicle routing problem has been analyzed through several studies which added a set of elements that provide a more qualified comprehension of the daily activity [14][15][16]. Yin and Chuang (2016) presented a new problem to study the least cost green vehicle of routing planning, where the vehicles transport the goods from the providers to the clients concerning the constraints of CO2 [14].…”

“…A novel contribution has been done by Aïder and Skoudarli (2020), in which the demand was included as a parameter of the method that used an epistemic representation [18]. Rizopoulos and Esztergár-Kiss (2020) developed an approach to model the behavior of electric vehicles concerning transport modes, charging stations, locations of the activities, and the departure times of the travelers [16]. However, MATSim which uses the co-evolutionary algorithm, was used to understand in more solid form the routing problems.…”

An Integrated Approach of Analytic Hierarchy Process and Triangular Fuzzy Sets for Analyzing the Park-and-Ride Facility Location Problem

“…Traffic saturation and pollution in cities caused by private vehicles have become commonplace in modern times. In order to mitigate these problems, some studies have proposed transport alternatives (e.g., pedestrian, bicycle, public transport, P&R) [13][14][15][16][17][18][19]. The alternative that has been poorly studied is the P&R system, in which one of the factors that requires comprehensive expertise is its location, which, through methods and methodologies, has allowed which parameters are considered to be determined.…”

Using Best Worst Method for Sustainable Park and Ride Facility Location

“…Among the chapters that integrate a SUMP, there are parking policies such as the Park and Ride (P&R) system, which provides an alternative for private vehicle users to change to a more sustainable transport mode and thus minimize the adverse effects of the private car in a central business district (CBD) [3,4]. The definition of the P&R system is that private vehicle users make their journey to the CBD in the following manner: a part of the journey is traveled by a private vehicle, and then it is parked to change to public transport to complete the journey [5,6]. According to this definition, a P&R system constitutes a type of transport that provides a modal transfer towards a more sustainable transport [7,8].…”