Traffic evacuation simulation based on multi-level driving decision model

Traffic evacuation is a critical task in disaster management. Planning its evacuation in advance requires taking many factors into consideration such as the destination shelter locations and numbers, the number of vehicles to clear, the traffic congestions as well as traffic road configurations. A traffic evacuation simulation tool can provide the emergency managers with the flexibility of exploring various scenarios for identifying more accurate model to plan their evacuation. This paper presents a traffic evacuation simulation system based on integrated multi-level driving-decision models which generate agents’ behavior in a unified framework. In this framework, each agent undergoes a Strategic, Cognitive, Tactical and Operational (SCTO) decision process, in order to make a driving decision. An agent’s actions are determined by a combination, on each process level, of various existing behavior models widely used in different driving simulation models. A wide spectrum of variability in each agent’s decision and driving behaviors, such as in pre-evacuation activities, in choice of route, and in the following or overtaking the car ahead, are represented in the SCTO decision process models to simulate various scenarios. We present the formal model for the agent and the multi-level decision models. A prototype simulation system that reflects the multi-level driving-decision process modeling is developed and implemented. Our SCTO framework is validated by comparing with MATSim tool, and the experimental results of evacuation simulation models are compared with the existing evacuation plan for densely populated Beijing, China in terms of various performance metrics. Our simulation system shows promising results to support emergency managers in designing and evaluating more realistic traffic evacuation plans with multi-level agent’s decision models that reflect different levels of individual variability of handling stress situations. The flexible combination of existing behavior and decision models can help generating the best evacuation plan to manage each crisis with unique characteristics, rather than resorting to a fixed evacuation plan.     

An empirical comparison of travel choice models that capture preferences for compromise alternatives

Compromise alternatives have an intermediate performance on each or most attributes rather than having a poor performance on some attributes and a strong performance on others. The relative popularity of compromise alternatives among decision-makers has been convincingly established in a wide range of decision contexts, while being largely ignored in travel behavior research. We discuss three (travel) choice models that capture a potential preference for compromise alternatives. One approach, which is introduced in this paper, involves the construction of a so-called compromise variable which indicates to what extent (i.e., on how many attributes) a given alternative is a compromise alternative in its choice set. Another approach consists of the recently introduced random regret-model form, where the popularity of compromise alternatives emerges endogenously from the regret minimization-based decision rule. A third approach consists of the contextual concavity model, which is known for favoring compromise alternatives by means of a locally concave utility function. Estimation results on a stated route choice dataset show that, in terms of model fit and predictive ability, the contextual concavity and random regret models appear to perform better than the model that contains an added compromise variable.     

Modelling lane changing and merging in microscopic traffic simulation

This paper introduces Simulation of Intelligent TRAnsport Systems (SITRAS), a massive multi-agent simulation system in which driver-vehicle objects are modelled as autonomous agents. The simulation outputs can be used for the evaluation of Intelligent Transport Systems applications such as congestion and incident management, public transport priority and dynamic route guidance. The model concepts and specifications, and the first applications of the model in the area of incident modelling in urban arterial networks were described in previous publications. This paper presents the details of the lane changing and merging algorithms developed for the SITRAS model. These models incorporate procedures for ‘forced’ and ‘co-operative’ lane changing which are essential for lane changing under congested (and incident-affected) traffic conditions. The paper describes the algorithms and presents simulation examples to demonstrate the effects of the implemented models. The results indicate that only the forced and cooperative lane changing models can produce realistic flow-speed relationships during congested conditions.     

Allowing for heterogeneous decision rules in discrete choice models: an approach and four case studies

The study of respondent heterogeneity is one of the main areas of research in the field of choice modelling. The general emphasis is on variations across respondents in relative taste parameters while maintaining the assumption of homogeneous utility maximising decision rules. While recent work has allowed for differences in the utility specification across respondents in the context of looking at heterogeneous information processing strategies, the underlying assumption that all respondents employ the same choice paradigm remains. This is despite evidence in the literature that different paradigms work differently well on given datasets. In this article, we argue that such differences may in fact extend to respondents within a single dataset. We accommodate these differences in a latent class model, where individual classes make use of different underlying paradigms. We present four applications using three different datasets, showing mixtures between “standard” random utility maximisation models and lexicography based models, models with multiple reference points, elimination by aspects models and random regret minimisation models. In each of the case studies, the behavioural mixing model obtains significant gains in fit over the base structure where all respondents are hypothesised to use the same rule. The findings offer important further insights into the behavioural patterns of respondents. There is also evidence that what is retrieved as taste heterogeneity in standard models may in fact be heterogeneity in decision rules.     

Application of advanced sampling for efficient probabilistic traffic modelling

In probabilistic traffic models, consideration of stochasticity in the dynamics of traffic gives a closer representation of a traffic system in comparison to that of a deterministic approach. Monte Carlo simulation is a broadly accepted method to consider variations in traffic within modelling. In this contribution, the possibility of increasing the efficiency of probabilistic traffic flow models using Monte Carlo simulation is analysed using variance reduction techniques and sequencing, for varied capacity and traffic demand values. The techniques of Importance Sampling, Latin Hypercube Sampling and Quasi-Random Sequencing are compared in a dynamic macroscopic traffic model to demonstrate the effectiveness of these techniques for reduction of the computational load when considering multiple input variations. Demonstration of their efficiency in traffic modelling is expected to lead to a wider application of the methods in practice.     

Validation of an agent based model using a participatory simulation gaming approach: The case of city logistics

Agent-based modeling is used for simulating the actions and interactions of autonomous entities aiming to assessing their effects on the system as a whole. At an abstract level, an agent-based model (ABM) is a representation of the many simple agents and interactions among them. The decision making of the agents is based on the rules given to them. In an ABM, the model output is the result of internal decision-making and may differ with alteration in the decision path. On the contrary, with the set of rules embedded in agents, their behavior is modeled to take a ‘certain action’ in a ‘certain situation’. It suggests that the internal decision making behavior of agents is truly responsible for the model output and thus it cannot be ignored while validating ABMs. This research article focuses on the validating agents’ behavior by evaluating decision-making processes of agents. For this purpose, we propose a validation framework based on a participatory simulation game. Using this framework we engage a human player (i.e. a domain stakeholder) to allow us to collect information about choices and validate the behavior of an individual agent. A proof-of-concept game is developed for a city logistics ABM to test the framework.     

Modeling discrete choice behavior using concepts from fuzzy set theory,approximate reasoning and neural networks

Models of discrete choice analysis are usually based on the random utility framework. They assume that decision makers make decisions that maximize their utility. Alternative formulations of the problem have also been proposed in the literature. These approaches model the decision makers’ perceptions of the attributes of the various alternatives using fuzzy sets and linguistic variables, and the decision process itself, using concepts from approximate reasoning and fuzzy control. The underlying assumption is that decision makers use a few simple rules that relate their vague perceptions of the various attributes to their preferences towards the available alternatives. The paper extends this approach by incorporating rule weights, which capture the importance of a particular rule in the decision process. It also presents an approach for calibrating the weights using concepts from neural networks. A case study, involving mode choice, is used to demonstrate the potential of the approach and compare it to alternative formulations and methodologies.     

Confidence intervals of willingness-to-pay for random coefficient logit models

Random coefficient logit (RCL) models containing random parameters are increasingly used for modelling travel choices. Willingness-to-pay (WTP) measures, such as the value of travel time savings (VTTS) are, in the case of RCL models estimated in preference space, ratios of random parameters. In this paper we apply the Delta method to compute the confidence intervals of such WTP measures, taking into account the variance–covariance matrix of the estimates of the distributional parameters. The same Delta method can be applied when the model is estimated in WTP space. Compared to simulation methods such as proposed by Krinsky and Robb, the Delta method is able to avoid most of the simulations by deriving partly analytical expressions for the standard errors. Examples of such computations are shown for different combinations of random distributions.     

Residential mobility and location choice: a nested logit model with sampling of alternatives

Residential mobility and relocation choice are essential parts of integrated transportation and land use models. These decision processes have been examined and modeled individually to a great extent but there remain gaps in the literature on the underlying behaviors that connect them. Households may partly base their decision to move from or stay at a current location on the price and quality of the available alternatives. Conversely, households that are on the market for a new location may evaluate housing choices relative to their previous residence. How and the degree to which these decisions relate to each other are, however, not completely understood. This research is intended to contribute to the body of empirical evidence that will help answer these questions. It is hypothesized that residential mobility and location choice are related household decisions that can be modeled together using a two-tier hierarchical structure. This paper presents a novel nested logit (NL) model with sampling of alternatives and a proposed procedure for sampling bias correction. The model was estimated using full-information maximum likelihood estimation methods. The results confirm the applicability of this NL model and support similar findings from other empirical studies in the residential mobility and location choice literature.     

Correcting for endogeneity in behavioral choice models with social influence variables

While psychologists and behavioral economists emphasize the importance of social influences, an outstanding issue is how to capture such influences in behavioral models used to inform urban planning and policy. In this paper we focus on operational models that do not require explicit knowledge of the individual networks of decision makers. We employ a field effect variable to capture social influences, which is calculated as the percent of population in the peer group that has chosen the specific alternative. We define the peer group based on socio-economic status and spatial proximity of residential location. As in behavioral economics and psychology, the concept is that one is influenced by the choices made by one’s peers. However, using such a social influence variable in a behavioral model causes complications because it is likely endogenous; unobserved factors that impact the peer group also influence the decision maker, yielding correlation between the field effect variable and the error. The contribution of this paper is the use of the Berry, Levinsohn, and Pakes (BLP) method to correct the endogeneity in a choice model. The two-stage BLP introduces constants for each peer group to remove the endogeneity from the choice model (where it is difficult to deal with) and insert it into a linear regression model (where endogeneity is relatively easier to deal with). We test the method using a mode choice data set from the Netherlands and readily available software and find there is an upward bias of the field effect parameter when endogeneity is not corrected. The procedure outlined presents a practical and tractable method for incorporating social influences in choice models.     

Instantaneous multihop connectivity of one-dimensional vehicular ad hoc networks with general distributions of communication nodes

Connected and automated vehicle technologies hold great promises for improving the safety, efficiency, and environmental impacts of the transportation sector. In this study we are concerned with multihop connectivity of instantaneous vehicular one-dimensional ad hoc networks (VANETs) formed by connected vehicles along a communication path in a road network with given either vehicle locations or traffic densities, market penetration rates, and transmission ranges. We first define a new random variable for the location of the end node of a communication chain, which is a discrete random variable with given vehicle locations and a mixed random variable with given traffic densities. Then recursive, iterative, or differential equation models of instantaneous multihop connectivity between two communication nodes are derived from the relationships between end node probability mass or density function and connectivity. Assuming a simple communication model, the new models are applicable for general distribution patterns of vehicles and communication nodes, including non-evenly placed vehicles and nonhomogeneous Poisson distributions of nodes. With given vehicle locations, the computational cost for this new model is linear to the number of vehicles; with given traffic densities, we derive a new closed-form connectivity model for homogeneous Poisson distributions of communication nodes and an approximate closed-form model when distribution patterns of communication nodes are given by spatial renewal processes. We then apply the models to evaluate impacts on connectivity of traffic patterns, including shock waves, and road-side stations. The connectivity model could be helpful for designing routing protocols in VANETs and developing their applications in transportation systems.     

Predicting the operational acceptance of airborne flight reroute requests using data mining

For tools that generate more efficient flight routes or reroute advisories, it is important to ensure compatibility of automation and autonomy decisions with human objectives so as to ensure acceptability by the human operators. In this paper, the authors developed a proof of concept predictor of operational acceptability for route changes during a flight. Such a capability could have applications in automation tools that identify more efficient routes around airspace impacted by weather or congestion and that better meet airline preferences. The predictor is based on applying data mining techniques, including logistic regression, a decision tree, a support vector machine, a random forest and Adaptive Boost, to historical flight plan amendment data reported during operations and field experiments. Cross validation was used for model development, while nested cross validation was used to validate the models. The model found to have the best performance in predicting air traffic controller acceptance or rejection of a route change, using the available data from Fort Worth Air Traffic Control Center and its adjacent Centers, was the random forest, with an F-score of 0.77. This result indicates that the operational acceptance of reroute requests does indeed have some level of predictability, and that, with suitable data, models can be trained to predict the operational acceptability of reroute requests. Such models may ultimately be used to inform route selection by decision support tools, contributing to the development of increasingly autonomous systems that are capable of routing aircraft with less human input than is currently the case.     

First-best marginal cost toll for a traffic network with stochastic demand

First-best marginal cost pricing (MCP) in traffic networks has been extensively studied with the assumption of deterministic travel demand. However, this assumption may not be realistic as a transportation network is exposed to various uncertainties. This paper investigates MCP in a traffic network under stochastic travel demand. Cases of both fixed and elastic demand are considered. In the fixed demand case, travel demand is represented as a random variable, whereas in the elastic demand case, a pre-specified random variable is introduced into the demand function. The paper also considers a set of assumptions of traveler behavior. In the first case, it is assumed that the traveler considers only the mean travel time in the route choice decision (risk-neutral behavior), and in the second, both the mean and the variance of travel time are introduced into the route choice model (risk-averse behavior). A closed-form formulation of the true marginal cost toll for the stochastic network (SN-MCP) is derived from the variational inequality conditions of the system optimum and user equilibrium assignments. The key finding is that the calculation of the SN-MCP model cannot be made by simply substituting related terms in the original MCP model by their expected values. The paper provides a general function of SN-MCP and derives the closed-form SN-MCP formulation for specific cases with lognormal and normal stochastic travel demand. Four numerical examples are explored to compare network performance under the SN-MCP and other toll regimes.     

Signalized intersections with variable flow rates: I analysis and simulation

One of the most common measures of signalized intersection operation is the amount of delay a vehicle incurs while passing through the intersection. Traditional models for estimating vehicle delay at intersections generally assume fixed signal timing and uniform arrival rates for vehicles approaching the intersection. One would expect that highly variable arrival rates would result in much longer delays than uniform arrival rates of the same average magnitude. Furthermore, one might expect that signal timing that is adjusted according to traffic volume would result in lower delay signal when variations in flow warrant such adjustable timing. This paper attempts to test several hypotheses concerning the effects of variable traffic arrival rates and adjusted signal timing through the use of simulation. The simulation results corroborate the hypothesis concerning the effect of varying arrival rates. As the variance of the arrival rate over time increases, the average delay per vehicle also increases. Signal timing adjustments based on traffic appear to decrease delay when flow rates vary greatly. As flow variations stabilize, the benefits of signal adjustments tend to diminish.     

A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice

The modeling of travel decision making has been a popular topic in transportation planning. Previous studies focused on random-utility discrete choice models and machine learning methods. This paper proposes a new modeling approach that utilizes a mixed Bayesian network (BN) for travel decision inference. The authors use a predetermined BN structure and calculate priori and posterior probability distributions of the decision alternatives based on the observed explanatory variables. As a “utility-free” decision inference method, the BN model releases the linear structure in the utility function but assumes the traffic level of service variables follow multivariate Gaussian distribution conditional on the choice variable. A real-world case study is conducted by using the regional travel survey data for a two-dimensional decision modeling of both departure time choice and travel mode choice. The results indicate that a two-dimensional mixed BN provides better accuracy than decision tree models and nested logit models. In addition, one can derive continuous elasticity with respect to each continuous explanatory variable for sensitivity analysis. This new approach addresses a research gap in probabilistic travel decision making modeling as well as two-dimensional travel decision modeling.     

A programmable calculation procedure for number of traffic conflict points at highway intersections

Traffic movement conflict points at intersections are the points at which traffic movements intersect (including crossing, merging, and diverging). Numbers and distribution of different types of conflict points are used to evaluate intersection access management designs and safety performance. Traditionally, the determination of the numbers of conflict points for different traffic movements is based on manual methods, which causes the difficulty for computerized procedures to evaluate safety performance of different access management designs. Sometimes, a programmable calculation procedure may provide more effective solutions as compared with manual methods. This paper presents a programmable calculation procedure for the determination of the numbers of conflict points, which could be used as a basis for a computerized procedure. Concepts of virtual movement lanes and intersection quadrants are introduced to specify types of intersections, traffic lane configurations, and traffic movement regulations. Calculation models, based on such concepts, for traffic movement conflict points at signalized and unsignalized intersections can be obtained. In support of the procedure, case studies are presented in the paper. The procedure presented in the paper can be programmed into a computer program for the purpose of a computerized evaluation of intersection safety and design performance of different access management or control approaches. Copyright © 2012 John Wiley & Sons, Ltd.     

A computationally efficient simulation model for estimating energy consumption of electric vehicles in the context of route planning applications

The fact that electric vehicles (EVs) are characterized by relatively short driving range not only signifies the importance of routing applications to compute energy efficient or optimal paths, but also underlines the necessity for realistic simulation models to estimate the energy consumption of EVs. To this end, the present paper introduces an accurate yet computationally efficient energy consumption model for EVs, based on generic high-level specifications and technical characteristics. The proposed model employs a dynamic approach to simulate the energy recuperation capability of the EV and takes into account motor overload conditions to represent the vehicle performance over highly demanding route sections. To validate the simulation model developed in this work, its output over nine typical driving cycles is compared to that of the Future Automotive Systems Technology Simulator (FASTSim), which is a simulation tool tested on the basis of real-world data from existing vehicles. The validation results show that the mean absolute error (MAE) of cumulative energy consumption is less than 45 W h on average, while the computation time to perform each driving cycle is of the order of tens of milliseconds, indicating that the developed model strikes a reasonable balance between efficacy of representation and computational efficiency. Comprehensive simulation results are presented in order to exemplify the key features of the model and analyze its output under specific highly aggressive driving cycles for road gradients ranging from −6% to 6%, in support of its usability as a practical solution for estimating the energy consumption in EV routing applications.     

A stochastic model of traffic flow: Theoretical foundations

In a variety of applications of traffic flow, including traffic simulation, real-time estimation and prediction, one requires a probabilistic model of traffic flow. The usual approach to constructing such models involves the addition of random noise terms to deterministic equations, which could lead to negative traffic densities and mean dynamics that are inconsistent with the original deterministic dynamics. This paper offers a new stochastic model of traffic flow that addresses these issues. The source of randomness in the proposed model is the uncertainty inherent in driver gap choice, which is represented by random state dependent vehicle time headways. A wide range of time headway distributions is allowed. From the random time headways, counting processes are defined, which represent cumulative flows across cell boundaries in a discrete space and continuous time conservation framework. We show that our construction implicitly ensures non-negativity of traffic densities and that the fluid limit of the stochastic model is consistent with cell transmission model (CTM) based deterministic dynamics.