Partha Chakroborty

Comparison of Pedestrian Fundamental Diagram Across Cultures

The relation between speed and density is connected with every self-organization phenomenon of pedestrian dynamics and offers the opportunity to analyze them quantitatively. But even for the simplest systems, like pedestrian streams in corridors, this fundamental relation is not completely understood. A comparison of data from literature shows that specifications in text books as well as measurements under various experimental conditions differ significantly. In this contribution it is studied whether cultural influences and length of the corridor can be the causes for these deviations. To reduce as much as possible unintentional effects, a system is chosen with reduced degrees of freedom and thus the most simple system, namely the movement of pedestrians along a line under closed boundary conditions. It is found that the speed of Indian test persons is less dependent on density than the speed of German test persons. Surprisingly the more unordered behavior of the Indians is more effective than the ordered behavior of the Germans. This may be due to differences in their self-organization behavior. Without any statistical measure one cannot conclude about whether there are differences or not. By hypothesis test it is found quantitatively that these differences exist, suggesting cultural differences in the fundamental diagram of pedestrians.

Genetic Algorithms for Optimal Urban Transit Network Design

This paper aims to highlight the effectiveness of genetic algorithm (GA)-based procedures in solving the urban transit network design problem (UTNDP). The analysis looks at why traditional methods have problems in solving the UTNDP. Procedures are suggested to alleviate these problems using a GA-based optimization technique. The main thrust of this work is three-fold: 1) to show the effectiveness of GAs in solving the UTNDP, 2) to identify features of the UTNDP that make it a difficult problem for traditional techniques, and 3) to suggest directions, through the presentation of GA-based methodologies for the UTNDP, for the development of GA-based procedures for solving other optimization problems having features similar to the UTNDP.

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).

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.

Time scheduling of transit systems with transfer considerations using genetic algorithms

Scheduling of a bus transit system must be formulated as an optimization problem, if the level of service to passengers is to be maximized within the available resources. In this paper, we present a formulation of a transit system scheduling problem with the objective of minimizing the overall waiting time of transferring and nontransferring passengers while satisfying a number of resource- and service-related constraints. It is observed that the number of variables and constraints for even a simple transit system (a single bus station with three routes) is too large to tackle using classical mixed-integer optimization techniques. The paper shows that genetic algorithms (GAs) are ideal for these problems, mainly because they (i) naturally handle binary variables, thereby taking care of transfer decision variables, which constitute the majority of the decision variables in the transit scheduling problem; and (ii) allow procedure-based declarations, thereby allowing complex algorithmic approaches (involving if then-else conditions) to be handled easily. The paper also shows how easily the same GA procedure with minimal modifications can handle a number of other more pragmatic extensions to the simple transit scheduling problem: buses with limited capacity, buses that do not arrive exactly as per scheduled times, and a multiple-station transit system having common routes among bus stations. Simulation results show the success of GAs in all these problems and suggest the application of GAs in more complex scheduling problems.

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.

OPTIMAL FLEET SIZE DISTRIBUTION AND SCHEDULING OF TRANSIT SYSTEMS USING GENETIC ALGORITHMS

Optimal fleet size distribution and scheduling with transfer consideration for a transit system is a difficult optimization problem. A traditional formulation of the problem leads to a large non‐linear mixed integer programming problem. Past experience has shown that traditional optimization methods are unable to give optimal solutions to even simpler versions of the problem (like the optimal scheduling problem with known fleet size distribution). In this paper, a simple binary coded genetic algorithm (GA) based approach to the optimization problem is presented. The use of GA allows a more efficient formulation of the problem and the GA based approach gives optimal/near‐optimal results with limited computation effort.

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.

A fuzzy inference based assignment algorithm to estimate O-D matrix from link volume counts

Origin destination trip demand matrix (O-D) is an essential ingredient in a wide variety of travel analysis and planning studies. The traditional way of obtaining O-D from home-interview survey data is expensive in terms of time and money. O-D estimation from link counts on the other hand is more appealing as the required data collection is simple and routine. Some of the O-D estimation methods which use link counts require the relative contributions of the O-D elements toward the link volumes. In the literature these proportions are assumed exogenous and are usually obtained from proportional traffic assignment models. In this study a flow dependent fuzzy inference based assignment algorithm is proposed for generating the required proportions and a recursive methodology is adopted for the O-D estimation. The methodology is applied to three test networks and the results are found to be satisfactory.

An asexual genetic algorithm for the general single vehicle routing problem

This article proposes an optimization algorithm for the general vehicle routing problem. The algorithm uses mutation based genetic algorithms (GAs) (or asexual GAs). The algorithm is general, in that it can handle various types of the vehicle routing problem; namely, the traveling salesman problem, the single vehicle pick-up and delivery problem, and the single vehicle pick-up and delivery problem with time windows. The algorithm is fast and gives optimal/near-optimal solutions with minimal computation effort. The algorithm is simple and easy to implement. Results from forty-six problems (most of which are obtained from existing sources) are also presented.