Stephen Arhin

Effectiveness and Acceptance of Adaptive Intelligent Speed Adaptation Systems

Intelligent speed adaptation (ISA) systems face significant consumer acceptance hurdles that limit the likelihood of widespread adoption, particularly in the United States. However, if these systems are designed as speed management systems rather than speed limiting systems, with adaptability to individual driving behavior, they may be more likely to meet with consumer acceptance. The results of a fixed-based driving simulator experiment that tested the acceptance and effectiveness of a new type of ISA, called an Advanced Vehicular Speed Adaptation System (AVSAS), are reported. The results of the experiment showed that AVSAS resulted in reductions in driver speeds across a range of roadway types. AVSAS is a speed management system that adapts to an individual drivers speed behavior and the current driving situation. AVSAS resulted in an average reduction of 5% of the maximum speeds and 3% of the average speeds of the drivers on four road segments. As expected, AVSAS did not reduce driver speeds as much as the mandatory control ISA system, and the experiment confirmed the results of tests conducted on ISA systems largely in Europe. Conversely, the results revealed that more participants were willing to purchase AVSAS compared with the information or mandatory ISAs. Although these results show the promise of a trade-off between system effectiveness and acceptability that has been missing in mandatory and information ISA research, AVSAS suggests that a range of ISA system design requirements could encourage the adoption of ISA systems in the United States.

Establishment of a Composite Safety Index for Pavement Management

One of the goals of local transportation agencies is to improve the quality of life for citizens and visitors by ensuring the efficient and safe movement of people and goods through the roadway system. Maintenance and rehabilitation of pavements are necessary to ensure that roadway networks continue to perform at their optimum. Currently, maintenance and rehabilitation of roadway networks depend on several factors including pavement condition indices, funding availability, among others. Previous studies have established relationships between crash frequency and pavement condition indices. However, the combined influence of speed, volume, and crash frequency on pavement indices, and thereby pavement management efforts has not been thoroughly examined. In this paper, a multinomial logistic regression was employed for 193 arterial segments to establish a new categorical variable: Composite Safety Index (CSI). The CSI values or ratings were based on pavement indices, crash frequency, traffic volumes and vehicular speeds to help categorize pavement sections for either maintenance or rehabilitation. The results indicated that the selected independent variables were statistically reliable in ranking pavement sections for rehabilitation or maintenance based on their CSI values.

Transit Bus Travel Time Prediction using AVL Data

The prediction of transit bus travel times along corridors is critical in the planning and operation of buses, especially in urban areas. Bus patrons tend to have more confidence in a transit system if travel times can be adequately predicted, within a certain margin of error. Washington DC’s the transit agency, the Washington Metropolitan Authority (WMATA), recently equipped some of its fleet with Automated Vehicle Location (AVL) systems and Passenger Count Systems (PCS) to obtain data as buses travel along corridors. In this study, data from the AVL/PCS system on transit buses were used to develop a travel time model to predict how long buses travel along selected corridors in Washington DC. AVL and PCS data for a period of one-month during the summer of 2016 for eight arterial bus routes used was in this study. The advertised travel times for the selected corridors from the selected origins and destinations were also obtained. Based on the literature review, a number of variables were selected as input for the prediction of bus travel times. From the data analysis, it was determined that the number of passengers alighting, passengers boarding, number of access approaches and signalized intersections, significantly predicted transit bus travel time at 95% confidence interval. In addition, the bus travel time prediction model was determined to be statistically significant with validation tests indication model adequacy at 5% level of significance. Keywords— Transit Travel Time; Travel Time Prediction, AVL

Prevailing Saturation Flow Rate for Lane Groups in an Urban Area

This study focused on determining the prevailing Saturation Flow Rate (SFR) for specific lane groups in an urban area: District of Columbia (DC). The lane groups considered were Through (T), shared Through and Right (TR), shared Through and Left (TL), and exclusive Left turn (L) lane groups. These SFR values could then be used to calculate the local base SFR. The study determined the prevailing SFR for these lane groups based on data collected at 81 intersections. The hypothesis that the mean SFRs for all the lane groups are different was tested at a 5% level of significance. From the results, the mean prevailing SFR for the T, TR, TL and L lane groups were 1,559, 1,461, 1,566 and 1,460 vphpl respectively. Those prevailing SFRs can be used for planning analyses in the District of Columbia. The results also indicated that these mean prevailing SFRs are statistically similar at 95% confidence interval. Based on the results, a local base SFR for the City can be determined for each lane group.

Predicting Dwell Time by Bus Stop Type and Time of the Day

Bus dwell time (DT), which is defined as the time interval between the opening and closing its doors to serve passengers at the bus stop, is an important element in improving the travel time between end terminals of bus routes. DT on bus routes in dense urban areas varies by time of day. Also, DT could be measured or estimated using mathematical models. This study aimed at developing innovative DT models for bus stops located in dense urban areas taking in consideration the bus stop type (located near intersections and at mid-blocks), and by time of the day (morning, mid-day and evening). The models were developed using simple ordinary least squares methods with all statistical inferences at 95% confidence interval. The results of the data analysis showed that DT, on average, was higher at bus stops near intersections than those at mid-blocks. The models obtained for DT were determined to be statistically significant at 95% confidence level, based on the R2, F-Statistic and model validation tests. The Kolmogorov-Smirnoff, normal probability and residual plots were used to confirm the adequacy of the models. The analysis also revealed that the models were significantly different by time of day and by bus stop type. It should also be noted that the models were based on bus transit operation in a dense urban area and may not be appropriate for predicting DT in non-similar settings.

Optimal Mix Designs for Pervious Concrete for an Urban Area

Pervious concrete is mixture of cement, aggregate, and water that provide a level of porosity which allows water to percolate into the sub-grade. It differs from the conventional concrete since it usually contains a smaller amount of fine aggregate. There is typically single size aggregate in pervious concrete which provides larger air void than conventional concrete to increase the rate of infiltration. Most jurisdictions have different pervious concrete mix designs. This research was aimed at developing and testing five design mixes of pervious concrete to identify the appropriate mix which would provide the maximum compressive strength with an acceptable permeability rate and flexural strength for the District of Columbia. The tests were conducted on the five design mixes using three different types of compaction methods (self-consolidating, half-rodding and Standard Proctor Hammer). Based on the results, a design mix with a compressive strength of 3,500 pounds per square inch (psi) with a maximum coefficient of permeability of 57.8 inches per hour (in/hr) was identified as the optimum. The maximum modulus of rupture of the selected mix was determined to be 565 psi. In-situ infiltration tests conducted of the pervious concrete installed at 3 locations in DC with the optimal pervious concrete mix yielded average infiltration rates between 86.1 and 208.7 in/hr. Keywords—Pervious Concrete,porosity, porous,mix design, permeability, stormwater