Nopadon Kronprasert

Energy and Environmental Assessment of High-Speed Roundabouts

Recently, an increased number of roundabouts have been implemented across the United States to improve intersection efficiency and safety. However, few studies have evaluated their energy and environmental impacts. Consequently, this study quantifies the energy and environmental impact of an isolated roundabout on a high-speed road by using second-by-second speed profiles derived from traffic simulation models in conjunction with microscopic energy and emission models. The study demonstrates that, at the intersection of a high-speed road with a low-speed road, an isolated roundabout does not necessarily reduce vehicle fuel consumption and emissions compared with other forms of intersection control (stop sign and traffic signal control). This case study found that the roundabout reduces the delay and queue lengths on the intersection approaches. However, the roundabout results in a significant increase in vehicle fuel consumption and emission levels compared with a two-way stop. The study demonstrates, for this case study, that the roundabout provides efficient movement of vehicles when the approach traffic volumes are relatively low. However, as demand increases, traffic at the roundabout experiences substantial increases in unnecessary delay in comparison with a strategy that uses signalized intersection control.

Lengths of Turn Lanes on Intersection Approaches: Three-Branch Fork Lanes—Left-Turn, Through, and Right-Turn Lanes

This paper examines the lengths of turn lanes when a single lane approaches a signalized intersection and is divided into three lanes: left-turn, through, and right-turn. These three lanes help to increase the capacity of the intersection by allowing more vehicles to enter the intersection during the green phase. The question is, What is the appropriate length of each turn lane? The vehicle queue pattern at the entrance to the turn lanes is examined first. The following conditions prohibit an arriving vehicle from entering the desired lane: (a) lane overflow (the lane is too short to accommodate all turning movements) and (b) lane entrance blockage (the overflow of the vehicles from other lanes blocks the entrance of the lane, even though space is available in that lane). A set of formulas to compute the probabilities of these events is developed. The lengths are calculated so that the probability that a lane does not overflow and the entrance of the lane is not blocked is greater than a threshold value. Tables of the recommended lengths are prepared for different turn volumes and threshold probabilities. These lengths are found to be shorter than what is traditionally recommended. The presence of three lanes (left-turn, through, and right-turn) allows arriving vehicles to spread among different lanes, and thus the chances of lane overflow and blockage are reduced. This is noticeable when the movements among the three directions are nearly even.

Determining Lengths of Left-Turn Lanes at Signalized Intersections Under Different Left-Turn Signal Schemes

An analytical procedure was developed to determine the lengths of left-turn lanes at signalized intersections. A large body of literature exists about this subject; however, most examine the case when the left-turn and through movements take place during the same signal phase (split phase). Many state departments of transportation base their guidelines on this case also. The general framework was developed for determining the lengths of the left-turn lanes that prevent lane overflow and blockage of the entrance of the left-turn lane by the queued through vehicles. The framework considers many factors: arrival rates and the sequence of left-turn and through vehicles, different signal schemes (the split-phase, permissive-only, protected-only leading, protected-only lagging, and protected-permissive left-turn phases), and intersection capacity. All possible queue patterns (including the leftover from the previous cycle) were identified, and the probabilities of lane blockage and lane overflow were obtained for different combinations of the parameters. The recommended lengths that prevent lane overflow and blockage of more than 95% of the cycles were developed. The lengths suggested by the existing guidelines were useful within certain ranges of combinations of left-turn and through volumes. Suggestions are made for determining the lengths for outside this limited range of combinations. The framework and suggested lengths should be useful for identifying the options of extending lane length or changing the signal scheme to manage lane overflow and blockage and for evaluating the adequacy of the current length.

Aggregate Blending Using Fuzzy Optimization

AbstractAggregate blending is a process that blends available aggregates to create a blend that meets gradation specifications while minimizing the unit cost of the blend. Today’s aggregate blending process is a multiobjective optimization problem that involves not only minimizing cost but also satisfying other specifications of the blend, such as fineness modulus, plasticity index, and specific gravity. Gradation limits are usually expressed in terms of the range of the percentage passing each sieve. In the traditional approach, rigid values are used for the gradation limits, and as a result, gradation and other specifications are frequently met at the limits during optimization, which is not particularly desirable. This paper proposes an approach for aggregate blending using fuzzy optimization method. The approach selects the best mix of aggregates such that not only are the individual gradation specifications met within the specification limits, but also their desirability within each range is satisfie...

Determining the Length of the Right-Turn Lane at a Signalized Intersection

This paper develops the analytical and computational processes for determining the length of the right-turn lane at a signalized intersection. It examines the factors that influence the length, reviews the available literature and practices, derives the recommended length analytically, develops a set of tables of the recommended lane lengths as a function of the approach volumes (right-turn, through-traffic, and cross-traffic volumes) and signal timing, and discusses the results. The analysis is done for two cases: when right-turn-on-red (RTOR) is not permitted and when it is permitted. The probabilities of the following events are computed: the right-turn vehicles do not overflow to the through lane and block the movement of through vehicles, and the queued through vehicles do not block the entrance of the right-turn lane. The lengths are derived to keep these probabilities greater than a threshold value. The recommended length is shown in the number of vehicle spaces, and a procedure that converts this number to the actual distance is presented. The current guidelines for lane length are based on the arrival rate of right-turn vehicles only. The arrival rate of the through vehicles must be considered also. As a result, the proposed lane length is different than in the existing guidelines. In addition, a new guideline of lane length is developed for the RTOR case.

Measuring Validity of Reasoning Process for Transportation Planning Using Bayesian Inference and Dempster-Shafer Theory

When evaluating the validity of the reasoning process of a transportation plan, a reasoning map facilitates discourse. If the truth values are attached to the premise and each link of the map, the overall truth of the reasoning process can be measured. The map is useful to examine the sensitivity of the truth with respect to the changes in knowledge and opinions. In calculating the truth, Bayesian inference measures it in probability measure. Demspter-Shafer (D-S) theory measures it in Belief and Plausibility. This paper examines the differences between these two methods using an example that deals with selection of the mode of public transportation in a large commercial complex.

Constructing Transit Origin–Destination Tables from Fragmented Data

This study proposes an approach that constructs the origin–destination table (O-D table) for urban bus or light rail lines from fragmented data about the number of boarding and alighting passengers at stops (B-A data) and the analysts spot knowledge about the trip pattern for selected O-D pairs. The B-A data of transit lines in the city center are often incomplete, yet they may be the only data available to characterize the passenger travel pattern. The proposed approach constructs the O-D table by using data that contain different levels of uncertainties and incompleteness. The model is based on two basic principles, maximum uncertainty and minimum uncertainty. The former is implemented by maximizing the entropy of the O-D table to derive the least-biased values. The latter refers to the maximum consistency with the available data including language-based knowledge about some of the O-D table elements. These principles are implemented by the multiobjective optimization structure. The model is found to be robust if it can incorporate various types of available data as well as the analysts knowledge. It was tested by using O-D data and B-A data from an actual transit operation. The quality of the derived O-D table is clearly related to the availability and the quality of the data; however, it can be improved significantly with the analysts spot knowledge about the values of selected O-D pairs. This method will open the way for transit planners to quickly develop a reasonable O-D table from the available incomplete data.

Reasoning-Building Process for Transportation Project Evaluation and Decision Making: Use of Reasoning Map and Evidence Theory

Policy makers for transportation investment projects engage in dialogues and debates in which reasonableness and clarity are of great value. In traditional transportation systems planning practices, stakeholders reason and provide evidence in support of their preferences, but these opinions often conflict and are rarely consistent. This paper presents a goal-oriented decision-making method for finding a transportation alternative that best achieves the projects goals and also indicates the level of stakeholders’ satisfaction. The proposed method (a) applies a reasoning map for structuring how experts and citizens perceive the alternatives for achieving the projects goals and (b) provides belief measures in evidence theory about to what extent the alternatives achieve the goals of the stakeholders. This method gives three kinds of results. First, the degrees of goal achievement can be calculated for the various stakeholders. Second, both the integrity of the reasoning and the quality of information are evaluated according to measures of uncertainty associated with this information. Finally, the critical reasoning links that matter most to goal achievement can be identified through sensitivity analysis. The paper applies the proposed method to evaluate a streetcar alternative against a bus rapid transit alternative in a real-world analysis of transit alternatives. The reasoning-building process allows planners and citizens to present their logic and justifications, promotes focused discourse of stakeholders, and enriches the quality of the planning and decision-making process.

Handling Uncertainty in Transit Project Evaluation and Rating Process: Comparison Between the Existing FTA Approach and a Fuzzy Inference Approach

A fuzzy inference approach that ranks proposals for major transit projects is proposed, and its performance is compared with the existing approach used by the FTA New Starts Program. The FTA approach uses a rigid mathematical process in which 24 attributes of a proposal are scored initially and then aggregated multiple times to obtain a single overall rating for the proposal. The proposals funding recommendation is based on this rating. In this approach, a small difference in the initial score can make a significant difference in the final score; that is, the final score is not stable with respect to small perturbations in the initial scores of the attributes. In any evaluation, there is always room for subjective judgment and associated uncertainty to enter when the score of an attribute, the break points on the scoring scale, and the value of the weights of the attributes are determined. The proposed fuzzy inference approach incorporates the fuzziness and approximation associated with the evaluation process and preserves them through the calculation process. In this study, data from the 2012 and 2013 reports of the FTA New Starts Program proposal evaluation were used to compare the FTA approach with the proposed approach. The proposed fuzzy inference approach was found to be robust in dealing with evaluator uncertainty in the initial scores. The final score of this method was found to be more stable than that of the FTA method with respect to small changes in initial values, weights, and break points on the performance scale.

Effects of data quality and quantity in systems modelling: a case study

When constructing a probability distribution from incomplete and imprecise data, the effects of the quantity and the quality of the data are of serious concern in practical applications. Consider a situation when one is building a matrix of a joint probability distribution. For some events, the probabilities are available only approximately, and for the majority of the events they are not available at all. Traditionally, if the known values are exact values, this type of problem is dealt with by maximizing the Shannon entropy of the distribution while using the known values as constraints. In this case, however, the available information is approximate and represented by fuzzy numbers. A multi-objective optimization method is proposed that employs the well-known principles of maximum and minimum uncertainty. In this method, the Shannon entropy is maximized and, in addition, the known elements whose membership grades are as high as possible are searched for. The method is applied to the construction of an origin–destination (O–D) table of a transit from incomplete and imprecise data. The behaviour of the solution with respect to quantity and quality of available data is tested with sensitivity analysis using real-world data of four transit lines. This analysis reveals how changes in the quantity and quality of data affect the acceptable level of an O–D table. Furthermore, the issue of how to combine O–D tables developed on the basis of different sets of approximate values is examined using a method that minimizes the sum of the relative Shannon entropies.