Shinya Kikuchi

Evaluation of the General Motors based car-following models and a proposed fuzzy inference model

Abstract This paper evaluates the properties of the General Motors (GM) based car-following models, identifies their characteristics, and proposes a fuzzy inference logic based model that can overcome some of the shortcomings of the GM based models. This process involves developing a framework for evaluating a car-following model and comparing the behavior predicted by the GM models with the behavior observed under the real world situation. For this purpose, an instrumented vehicle was used to collect data on the headway and speeds of two consecutive vehicles under actual traffic conditions. Shortcomings of the existing GM based models are identified, in particular, the stability conditions were analyzed in detail. A fuzzy-inference based model of car-following is developed to represent the approximate nature of stimulus–response process during driving. This model is evaluated using the same evaluation framework used for the GM models and the data obtained by the instrumented test vehicle. Comparison between the performance of the two models show that the proposed fuzzy inference model can overcome many shortcomings of the GM based car-following models, and can be useful for developing the algorithm for the adaptive cruise control for automated highway system (AHS).

Transportation route choice model using fuzzy inference technique

Fuzzy set theory is applied to solve the problem of traffic assignment between two alternative routes on a highway network. The drivers perceived travel time on each route is treated as a fuzzy number, and his choice of route is based on an approximate reasoning model. The model consists of rules which indicate the degree of preference for each route given the approximate travel time of the two routes. Applying the model to each driver and then aggregating the individual preferences, a fuzzy network loading algorithm assigns traffic to each route.<<ETX>>

USING BUS TRAVEL TIME DATA TO ESTIMATE TRAVEL TIMES ON URBAN CORRIDORS

Obtaining near real-time information of travel times is a critical element of most applications of intelligent transportation systems. The use of transit vehicles as probe vehicles for collecting travel time data for automobiles on urban corridors was examined. Because transit vehicles are increasingly equipped with an automated vehicle locator (AVL) for reporting the current location of the vehicle, it may be possible to use the AVL data for travel time purposes. In anticipation of such an application of AVL, the relationship between travel times of a transit vehicle and of an automobile is examined for stability of data and adjustment needs. Travel times of transit vehicles and automobiles were measured simultaneously along the same sections on major corridors in Delaware. The difference in travel times was relatively stable, and, hence, appropriate formulas for predicting the travel time of automobiles were developed. The model coefficients were found to be reasonable and stable for various traffic conditions. The study suggests that the AVL-equipped transit vehicle can be used as a probe vehicle to collect travel time data at regular intervals with minimum cost.

Application of Fuzzy Logic to the Control of a Pedestrian Crossing Signal

Fuzzy logic is known to be suited for dealing with a complex optimization problem with many objectives, many constraints, unclear input information, and vague decision criteria. Controlling the timing of a traffic signal falls in this category of problem. Fuzzy logic is introduced for controlling the timing of a pedestrian crossing signal. The controller is designed to emulate the decision process of an experienced crossing guard. The performance of this control is tested against two types of conventional demand-actuated control: one that uses the traditional green extension and the other that uses modified extension rules. The criteria for evaluation are delays to the pedestrians and the vehicles, and the percentage of vehicles that are stopped. The fuzzy logic controller finds a compromise between two conflicting objectives: minimization of pedestrian delay and minimization of vehicular delay and stops. The evaluation was performed using a microscopic simulation called HUTSIM developed at the Helsinki University of Technology. The fuzzy logic controller performs equally well as or better than conventional demand-actuated control without requiring many parameter settings. Furthermore, the control rules are simple and a compilation of rational decision processes is expressed in natural language.

A method to defuzzify the fuzzy number: transportation problem application

In many problems of transportation engineering and planning, we encounter situations, in which the observed or derived values of the variables are approximate, yet the variables themselves must satisfy a set of rigid relationships dictated by physical principle. When the observed values do not satisfy the relationships, each value is adjusted until they satisfy the relationship. We propose a simple adjustment method that finds the most appropriate set of crisp numbers. The method assumes that each observed value is an approximate number (or a fuzzy number) and the true value is found in the support of the membership function. For each of many possible sets of values that satisfy the relationships, the lowest membership grade is checked and the set whose lowest membership grade is the highest is chosen as the best set of values for the problem. This process is performed using the fuzzy linear programming method. The paper presents the model, the computational process and applications.

Application of fuzzy sets theory to the saving based vehicle routing algorithm

Abstract The basic input data for any vehicle routing models are travel time, distances, and cost between nodes in the network. Information on these basic data is not always accurate, since many fa...

Lengths of Double or Dual Left-Turn Lanes

Double (or dual) left-turn lanes (DLTLs) are a relatively new geometric feature, and the literature on their design parameters is limited. The effectiveness of the DLTL in improving the operation of an intersection depends on several design parameters; among them, the most critical is the length of the DLTL. A procedure for determining the length of the DLTL was developed. First, the procedure surveys how drivers choose a lane of the DLTL in the real world and analyzes the relationship between lane use and the volume of left-turn vehicles. Second, the procedure formulates the probability that all arriving left-turn vehicles during the red phase can enter the left-turn lanes; this means no overflow of left-turn vehicles from the DLTL and no blockage of the entrance of the DLTL by the queue of through vehicles. This probability is presented as a function of the length of the DLTL and the arrival rates of left-turn and through vehicles. The adequate lane length is derived such that the probability of the vehicles entering the DLTL is greater than a threshold value. Third, the adequate length is expressed in number of vehicles; later, this value is converted to the actual distance required on the basis of the vehicle mix and preference between the two lanes. Recommended lengths are presented as a function of left-turn and through volumes for practical application. The proposed approach is unique in that it avoids lane overflow and blockage of lane entrance.

DEVELOPING A MEASURE OF TRAFFIC CONGESTION: FUZZY INFERENCE APPROACH

Many measures have been proposed to represent the status of traffic conditions on arterial roadways in urban areas. The debate about what is the most appropriate measure continues. In a contribution to the debate, another approach was offered. Traditionally, two general approaches exist. One is based on the relationship between supply and demand. The other is a measure relative to the most acceptable status of service quality. The latter measure allows the public to relate to their travel experience. In either case, however, derivation of measures of congestion involves uncertainty because of imprecision of the measurement, the traveler’s perception of acceptability, variation in sample data, and the analyst’s uncertainty about causal relations. A measure is proposed that is a composite of two traditional measures, travel speed and delay. In recognition of the uncertainty, a fuzzy inference process was proposed. The inputs are travel speed, free-flow speed, and the proportion of very low speed in the total travel time. These values were processed through fuzzyrule-based inference. The outcome was a single congestion index value between 0 and 1, where 0 is the best condition and 1 is the worst condition. The process was demonstrated using real-world data. The results were compared with those of the Highway Capacity Manual. Although no conclusion can be drawn about the best measure of congestion, the proposed inference process allows the mechanism to combine different measures and also to incorporate the uncertainty in the individual measures so that the composite picture of congestion can be reproduced.

CALIBRATING THE MEMBERSHIP FUNCTIONS OF THE FUZZY INFERENCE SYSTEM: INSTANTIATED BY CAR-FOLLOWING DATA

Abstract The fuzzy rule based inference is known to be a useful tool to capture the behavior of an approximate system in transportation. One of the obstacles of implementing the fuzzy rule based inference, however, has been to calibrate the membership functions of the fuzzy sets used in the rules. This paper proposes a way to calibrate the membership function when a set of input and output data is given for the system. First, the mathematical operations of the fuzzy rule based inference system are represented by a neural network construction. The operations of each node of this neural network are designed so that they correspond to specific logical operations of the fuzzy rule based inference system. The values of the weights of this neural network are set to correspond to the parameters that control the shape and location of each membership function. Second, given a set of input–output data, the weights are corrected sequentially using the principle of the generalized delta rule based back-propagation mechanism. After correction, the values of the weights are used to specify the exact shape of the membership functions of the fuzzy sets in the rules. The procedure implements a set of logical rules that can be applied when calibrating the shapes of the membership functions of a fuzzy inference system. An example, in which the membership functions of a fuzzy inference model for car-following behavior are calibrated using the real world data, is shown.

Examination of Methods That Adjust Observed Traffic Volumes on a Network

As the models of transportation planning and engineering become more and more sophisticated, the quality of data that is used as input to the models is of critical importance to the integrity of the analysis. Several methods that adjust the field-traffic-volume data so that they become consistent and useful information for the subsequent analysis steps are examined. Consistency as satisfaction of flow conservation and other relationships underlying the network flow in question are defined. Six methods, including the manual method, are presented, and the logic and computational process are explained for each method. Except for the manual method, the methods are grouped into two general categories and discussed—one considers inconsistency in the data as a result of the statistical error and uses the classical-regression approach, and the other considers the observed value as the approximate value and uses the fuzzy-set theory. These methods are compared for their performance using an example problem. Transportation analysts’ alternative methods for volume adjustment are provided, and the analysts’ theoretical and practical implications are explained.