Loukas Dimitriou

Fuzzy Modeling Approach for Combined Forecasting of Urban Traffic Flow

This paper looks at the problem of accuracy of short-term traffic flow forecasting in the complex case of urban signalized arterial networks. A new, artificial intelligence-based approach is offered for improving accuracy of traffic predictions through suitably combining forecasts derived from a set of individual predictors. This approach employs a fuzzy rule-based system (FRBS), which is augmented with an appropriate metaheuristic (direct search) technique to automate the tuning of the system parameters within an online adaptive rolling horizon framework. The proposed hybrid FRBS is used to nonlinearly combine traffic flow forecasts resulting from an online adaptive Kalman filter and an artificial neural network model. Empirical results obtained from the models implementation into an actual urban signalized arterial show the ability of the proposed approach to considerably overperform the given individual traffic predictors.

Reliable stochastic design of road network systems

This paper investigates the continuous version of the stochastic Network Design Problem (NDP) with reliability requirements. The problem is considered as a two-stage Stackelberg game with complete information and is formulated as a stochastic bi-level programming problem, which is extended to include reliability as well as physical and budget constraints. The estimation procedure combines the use of Monte Carlo simulation for modelling the stochastic nature of the system variables with a Genetic Algorithm (GA), for treating the complexity of this new formulation. The computational experience obtained from a test road network application demonstrates the ability of the proposed methodology to address the need for incorporating reliability requirements and stochasticity into the various system components in the design process. The results can provide useful insight into the evaluation of alternative reliable network capacity improvement plans under the effect of uncertainty on the demand, supply and route choice process of travellers.

Genetic computation of road network design and pricing Stackelberg games with multi-class users

This paper deals with the problems of optimal capacity and pricing decisions in private road networks. These problems are described as a class of design and pricing Stackelberg games and formulated as nonconvex, bilevel nonlinear programs. Such games capture interactions among the decisions of system designer/operator, government regulations and reactions of multi-class users on optimal toll-capacity combinations. The present class of games applies to a realistic urban highway with untolled alternative arterial links. In contrast with the mostly used continuous representations, the highway capacity is more intuitively expressed as a discrete variable, which further complicates the solution procedure. Hence, an evolutionary computing approach is employed to provide a stochastic global search of the optimal toll and capacity choices. The results offer valuable insights into how investment and pricing strategies can be deployed in regulated private road networks.

An agent-based framework for designing competitive transportation systems

This paper deals with the case of programming the development of future transportation systems by identifying inter-dependencies among competitors. Here, a market of maritime facilities is modelled as an n-person non-cooperative game among port authorities that promote the attractiveness of their terminal facilities, in terms of level of service provided. At the same time, freight shippers/carriers who are forming their service network based on the prevailing conditions offered by the available transportation paths are also modelled in the context of a non-cooperative game. Optimal decisions are obtained by extending the standard single leader–multiple followers Stackelberg game-theoretic formulation of the network design problem (NDP) to its complete form of multiple leaders–multiple followers competitive NDP. The estimation of the equilibrium point of the above complex transportation system is based on a novel evolutionary optimisation framework. Results from alternative design strategies are presented revealing the effects of competition and cooperation on systems design.

Fuzzy Rule-Based System Approach to Combining Traffic Count Forecasts

Current advances in artificial intelligence are providing new opportunities for utilizing the enormous amount of data available in contemporary urban road surveillance systems. Several approaches, methodologies, and techniques have been presented for analyzing and forecasting traffic counts because such information has been identified as vital for the deployment of advanced transportation management and information systems. In this paper, a meta-analysis framework is presented for improving forecasted information of traffic counts, based on an adaptive data processing scheme. In particular, a framework for combining traffic count forecasts within a Mamdani-type fuzzy adaptive optimal control scheme is presented and analyzed. The proposed methodology treats the uncertainty pertaining to such circumstances by augmenting qualitative information of future traffic flow states (and values) with a knowledge base and a heuristic optimization routine that provides dynamic training capabilities, resulting in an efficient real-time forecasting mechanism. Results from the application of the proposed framework on data acquired from realistic signalized urban network data (of Athens, Greece) and for a diversity of locations exhibit its potential.

Simultaneous Origin-Destination Matrix Estimation in Dynamic Traffic Networks with Evolutionary Computing

This paper presents an evolutionary computing approach for the estimation of dynamic Origin-Destination (O-D) trip matrices from automatic traffic counts in urban networks. A multi-objective, simultaneous optimization problem is formulated to obtain a mutually consistent solution between the resulting O-D matrix and the path/link flow loading pattern. A genetically augmented microscopic simulation procedure is used to determine the path flow pattern between each O-D pair by estimating the set of turning proportions at each intersection. The proposed approach circumvents the restrictions associated with employing a user-optimal Dynamic Traffic Assignment (DTA) procedure and provides a stochastic global search of the optimal O-D trip and turning flow distributions. The application of the model into a real arterial street sub-network demonstrates its ability to provide results of satisfactory accuracy within fast computing speeds and, hence, its potential usefulness to support the deployment of dynamic urban traffic management systems.

Inferring Activities from Social Media Data

Social media produce an unprecedented amount of information that can be extracted and used in transportation research, with one of the most promising areas being the inference of individuals’ activities. Whereas most studies in the literature focus on the direct use of social media data, this study presents an efficient framework that follows a user-centric approach for the inference of users’ activities from social media data. The framework was applied to data from Twitter, combined with inferred data from Foursquare that contains information about the type of location visited. The users’ data were then classified with a density-based spatial classification algorithm that allows for the definition of commonly visited locations, and the individual-based data were augmented with the known activity definition from Foursquare. On the basis of the known activities and the Twitter text, a set of classification algorithms was applied for the inference of activities. The results are discussed according to the types of activities recognized and the classification performance. The classification results allow for a wide application of the framework in the exploration of the activity space of individuals.

Optimal Co-evolutionary Strategies for the Competitive Maritime Network Design Problem

The current paper is focusing into the less well-defined transportation networks as those that are formed by the integration (combination) of alternative transportation means for servicing freight movements and the special inter-dependencies that are developed by this integration. Here the market of maritime facilities is modelled as an n-person non-cooperative game among port authorities who control the attractiveness of their terminal facilities. By taking the above interdependencies into consideration, optimal decisions of port authorities are obtained by extending the classical single leader-multiple followers Stackelberg game-theoretic formulation of the Network Design Problem (NDP) to its complete form of multiple leaders-multiple followers Competitive NDP (CNDP). The estimation of the equilibrium point of the above formulation is made by incorporating a novel evolutionary game-theoretic genetic operator into a hybrid Genetic Algorithm. The results from the application of the proposed framework into a realistic part of the East Mediterranean freight network show the potential of the method to support decisions of port authorities concerning future infrastructure investments.

Genetically Optimized Infrastructure Design Strategies in Degradable Transport Networks

This chapter examines the problem of the resource allocation in degradable road transport networks within a stochastic evolutionary optimization framework. This framework expresses the stochastic equilibrium Network Design Problem (NDP) as a game-theoretic, combinatorial bi-level program. Both the discrete and continuous versions of the reliable NDP are considered in order to address different strategies of network infrastructure investment. The estimation procedure employs a Latin Hypercube sampling method for simulating degradation-inducing variations in users’ attributes and system characteristics, and hence evaluates the network travel time reliability which constrains the solution. This simulation-based risk assessment technique is combined with a genetic algorithm to handle the complex, non-convex nature of the NDP adequately. The test implementation of the proposed framework demonstrates the significant role of incorporating the stochasticity and reliability requirements in the design process to facilitate the selection of the optimal investment strategies in degradable road networks.

GENETIC-ALGORITHM-BASED MICRO-SIMULATION APPROACH FOR ESTIMATING TURNING PROPORTIONS AT SIGNALIZED INTERSECTIONS

Abstract This paper presents a method for the automatic estimation of turning proportions at signalized urban intersections using partial link count information. The method integrates a microscopic model to simulate the network traffic conditions with a genetic algorithm to minimize the difference between estimated and observed movement flows. The implementation into a real signalized arterial network showed that the method can reproduce observed flows with a reasonable accuracy and computing speed that allows its usage in real-time urban traffic control systems.