Adding Electric Vehicle Modeling Capability to an Agent-Based Transport Simulation

Battery-electric and plug-in hybrid-electric vehicles are envisioned by many as a way to reduce CO2 traffic emissions, support the integration of renewable electricity generation, and increase energy security. Electric vehicle modeling is an active field of research, especially with regards to assessing the impact of electric vehicles on the electricity network. However, as highlighted in this chapter, there is a lack of capability for detailed electricity demand and supply modeling. One reason for this, as pointed out in this chapter, is that such modeling requires an interdisciplinary approach and a possibility to reuse and integrate existing models. In order to solve this problem, a framework for electric vehicle modeling is presented, which provides strong capabilities for detailed electricity demand modeling. It is built on an agent-based travel demand and traffic simulation. A case study for the city of Zurich is presented, which highlights the capabilities of the framework to uncover possible bottlenecks in the electricity network and detailed fleet simulation for CO2 emission calculations, and thus its power to support policy makers in taking decisions.

Activity-Based Travel Demand Forecasting Using Micro-Simulation

Activity-based models of travel demand employ in most cases a micro-simulation approach, thereby inevitably including a stochastic error that is caused by the statistical distributions of random components. As a result, running a transport micro-simulation model several times with the same input will generate different outputs. In order to take the variation of outputs in each model run into account, a common approach is to run the model multiple times and to use the average value of the results. The question then becomes: What is the minimum number of model runs required to reach a stable result? In this chapter, systematic experiments are carried out by using the FEATHERS, an activity-based micro-simulation modeling framework currently implemented for Flanders (Belgium). Six levels of geographic detail are taken into account, which are building block level, subzone level, zone level, superzone level, province level, and the whole Flanders. Three travel indices (i.e., the average daily number of activities per person, the average daily number of trips per person, and the average daily distance travelled per person), as well as their corresponding segmentations with respect to socio-demographic variables, transport mode alternatives, and activity types are calculated by running the model 100 times. The results show that application of the FEATHERS at a highly aggregated level only requires limited model runs. However, when a more disaggregated level is considered (the degree of the aggregation here not only refers to the size of the geographical scale, but also to the detailed extent of the index), a larger number of model runs is needed to ensure confidence of a certain percentile of zones at this level to be stable. The values listed in this chapter can be consulted as a reference for those who plan to use the FEATHERS framework, while for the other activity-based models the methodology proposed in this chapter can be repeated.

Mobility Profiling

The ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network.

Synthetic Population Techniques in Activity-Based Research

Activity-based approach, which aims to estimate an individual induced traffic demand derived from activities, has been applied for traffic demand forecast research. The activity-based approach normally uses two types of input data: daily activity-trip schedule and population data, as well as environment information. In general, it seems hard to use those data because of privacy protection and expense. Therefore, it is indispensable to find an alternative source to population data. A synthetic population technique provides a solution to this problem. Previous research has already developed a few techniques for generating a synthetic population (e.g. IPF [Iterative Proportional Fitting] and CO [Combinatorial Optimization]), and the synthetic population techniques have been applied for the activity-based research in transportation. However, using those techniques is not easy for non-expert researchers not only due to the fact that there are no explicit terminologies and concrete solutions to existing issues, but also every synthetic population technique uses different types of data. In this sense, this chapter provides a potential reader with a guideline for using the synthetic population techniques by introducing terminologies, related research, and giving an account for the working process to create a synthetic population for Flanders in Belgium, problematic issues, and solutions.

The Evolution from Electric Grid to Smart Grid

In this chapter, an overview of electric power systems is presented. The purpose is to describe the structure and operation of the power system and its evolution to the new smart grids. The first section gives an introduction about the electric grid and its evolution. Then, there is a section with a brief description of the different components of the electric power system: generation, transmission, distribution, and consumption. The third section is related to power system control, explaining why control actions are necessary in the power system to maintain the balance between supply and consumption and to keep constant the system frequency (at 50 or 60 Hz). In order to understand future applications of electric vehicles, it is important to present a fourth section related to fundamentals of the electricity markets. The chapter finishes with a description of the future power systems with high penetration of intermittent renewable energies, energy storage capacity, active demand management, and integration with telecommunication infrastructure.

Traffic Safety Implications of Travel Demand Management Policies

Travel Demand Management (TDM) consists of a variety of policy measures that affect the transportation system’s effectiveness by changing travel behavior. Although the primary objective to implement such TDM strategies is not to improve traffic safety, their impact on traffic safety should not be neglected. The main purpose of this study is to investigate differences in the traffic safety consequences of two TDM scenarios: a fuel-cost increase scenario (i.e. increasing the fuel price by 20%) and a teleworking scenario (i.e. 5% of the working population engages in teleworking). Since TDM strategies are usually conducted at a geographically aggregated level, crash prediction models that are used to evaluate such strategies should also be developed at an aggregate level. Moreover, given that crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is also examined. The results indicate the necessity of accounting for the spatial correlation when developing crash prediction models. Therefore, Zonal Crash Prediction Models (ZCPMs) within the geographically weighted generalized linear modeling framework are developed to incorporate the spatial variations in association between the Number Of Crashes (NOCs) (including fatal, severe, and slight injury crashes recorded between 2004 and 2007) and a set of explanatory variables. Different exposure, network, and socio-demographic variables of 2200 traffic analysis zones in Flanders, Belgium, are considered as predictors of crashes. An activity-based transportation model is adopted to produce exposure metrics. This enables a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models. In this chapter, several ZCPMs with different severity levels and crash types are developed to predict the NOCs. The results show considerable traffic safety benefits of conducting both TDM scenarios at an average level. However, there are certain differences when considering changes in NOCs by different crash types.

Issues in Feathers Application in the Seoul Metropolitan Area

Over the last decades, the trip-based approach, also known as the four-step model, has been playing an unrivaled role in transportation demand research in Korea. It has been used to predict changes in traffic volume resulting from new transportation policy measures, and also has allowed conducting benefit-cost analyses for new infrastructure provisions. It has been increasingly difficult for the trip-based model to anticipate individual responses to new transportation policy inputs and infrastructure provision as the society becomes personalized and diversified. Activity-Based Modeling (ABM) approaches, predicting travel demand derived from individual activity participations, were introduced to complement the trip-based approach in this regard. The chapter introduces the Seoul ABM project that aims to first apply FEATHERS as an ABM to the data collected in Seoul Metropolitan Area (SMA) and then develop a prototype of the ABM framework for Korea. More specifically, the chapter first briefly describes SMA in comparison with Flanders in Belgium and other countries. It then introduces related research works in Korea and the background of the Seoul ABM project. After these, a FEATHERS framework applied for the Seoul ABM project is described with its data requirements. Major issues of and solutions to the Seoul ABM project are then discussed with regard to the data preprocessing. The chapter ends with a summary and future work.

A Driver’s Mind

Over the last decade, traffic simulation frameworks have advanced into an indispensible tool for traffic planning and infrastructure management. For these simulations, sophisticated models are used to “mimic” traffic systems in a lifelike fashion. In most cases, these models focus on a rather technical scope. Human factors, such as drivers’ behaviours are either neglected or “estimated” without any proven connection to reality. This chapter presents an analysis of psychological driver models in order to establish such a connection. In order to do so, human driver behaviour is introduced from a psychological point of view, and state-of-the-art conceptualisations are analysed to identify factors that determine human traffic behaviour. These factors are explained in more detail, and their appliances in human behaviour models for traffic simulations are discussed. This chapter does not provide a comprehensive mapping from simulation requirements to particular characteristics of human driver behaviour but clarifies the assembly of human traffic behaviour, identifies relevant factors of influence, and thus, serves as a guideline for the development of human behaviour models for traffic simulations.

An Effective Methodology for Road Accident Data Collection in Developing Countries

Underreporting of road accidents has been widely accepted as a common phenomenon. In many developing countries this remains a critical problem as inappropriate information regarding road accidents does not provide a base to analyse its root causes. Therefore, effectiveness of implemented interventions are always questionable. In Pakistan, responsibility of collecting initial information regarding road accidents lies with the Police Department; however, reported figures are reflecting underestimation of the situation. This chapter reports the effectiveness of prevailing approaches for recording accident information in developing countries like Pakistan, India and Bangladesh, etc. Furthermore, it presents a unique methodology that has been adopted in Karachi for recording road accident information through an institute established on the notions of public-private partnership. Various features of that unique data collection mechanism are presented along with the discussion of some success stories, where the collected data has contributed significantly in improving road safety conditions.

Impacts of Electric Mobility on the Electric Grid

Electric mobility is becoming an option for reducing greenhouse gas emissions of road transport and decreasing the external dependence on fossil fuels. However, this new kind of mobility will introduce additional loads to the power system, and it is important to determine its effects on it. As a direct scenario from DATA SIM FP7 EU project, an application related to electric mobility and its impact on the electric grid from Flanders region is presented in this chapter. The chapter begins with a brief description of the electric transmission network for Flanders region and the electric vehicles energy requirements for different mobility zones in this region, obtained from FEATHERS, an activity-based model. In the following section, the main assumptions that allow estimating the total electricity consumption for each mobility area is presented. Once this total consumption per zone has been estimated, an algorithm to link the mobility areas with the nearest substation is developed. Finally, the impact of charging electric vehicles on the transmission substations is examined.