Bashar Al-Omari

Models for Right-Turn-on-Red and Their Effects on Intersection Delay

Models to estimate right-turn-on-red (RTOR) volumes at intersections with exclusive right-turn (RT) lanes are developed, and the effects of RTOR volumes on computed delay are assessed. The important variables in these models are the RT volume, followed by green-time-to-cycle (G/C) ratio, volume of conflicting traffic, and whether there is a protected phase for opposing left-turning vehicles. The estimated RTOR increased as the RTs increased. However, it decreased as G/C and the volume of conflicting traffic increased. Results show that not accounting for RTOR volumes can lead to a significant difference in delay estimates for RT lanes and, to a lesser extent, on the corresponding approaches. For RT lanes, in one-half of the cases the difference was greater than 5 percent, in more than one-quarter of the cases the difference was greater than 10 percent, and in at least one of eight cases the difference was greater than 20 percent. Differences for individual cases ranged between 0 and 130 percent, with an average of 12 percent. For approaches, the average delay difference was 4 percent, and for individual cases the difference ranged between −2 and 78 percent. As recommended by the Highway Capacity Manual, actual field counts of RTOR volume should be used whenever available. However, in the absence of such counts, the models developed here can be used and hence should be considered in capacity analysis procedures.

RED LIGHT VIOLATIONS AT RURAL AND SUBURBAN SIGNALIZED INTERSECTIONS IN JORDAN

This case study investigated red light violations at rural and suburban signalized intersections in Jordan. Field observations were conducted at 15 signalized intersections located in different Jordanian regions: Amman, Irbid, and Zarqa. The results showed that, out of a total of 1,190 drivers who had a chance for violation, 153 (12.9%) drivers ran red lights. It was found that older drivers have less tendency for running red lights. Based on vehicle type, the analysis showed that truck drivers had the highest violation rate followed by small vehicles and then buses. The Y-shaped intersection had a higher percentage of violations as compared to the T- and cruciform-shaped intersections. The percentage of red light violations was found to be directly proportional to the subjects approach speed and inversely proportional to the conflicting traffic volumes.

Estimation of Delay Induced by Downstream Operations at Signalized Intersections over Extended Control Time

Models are presented to estimate delay during extended control periods (multiple cycles) when downstream traffic queues are both changing over time and significant enough to disrupt traffic flow from an upstream intersection. The models consist of an upstream demand estimation algorithm, a downstream queue build-up prediction algorithm, a shockwaves propagation and dissipation tracking algorithm, and a mechanism to explicitly capture and feed traffic conditions of current cycles into subsequent cycles control design. Basic traffic flow properties, signal control parameters, and link geometry are used as inputs. The models are modular and can be incorporated in any size system for one or multiple cycles. The models were applied to a hypothetical system of closely spaced intersections and tested for different traffic flow, control, and geometric conditions. The results show that the delay induced by downstream traffic operations on an upstream intersection can be significant and that it may change with each cycle and could reach equilibrium once traffic flow, downstream queues, and signal control measures stabilize and start replicating over time. The results show that the green ratio, offsets, and spacing between intersections have significant effects on this delay. The macroscopic delay models are validated using a microscopic traffic simulation model. There is a close association between delay and queue lengths from both models.

Configuring microgenetic algorithms for solving traffic control problems: the case of number of generations

Efficient and successful use of genetic algorithms (GAs) requires careful selection of several parameter values. One such critical parameter is the processing time (or, number of generations) that is sufficient to ensure suitable convergence. Todate there is only limited guidance on this subject, and in most cases detailed knowledge of the structure and properties of the problem is necessary for such guidance to be useable. For real world problems such knowledge may not be readily available. We describe an experimental approach to establish relationships between time to convergence and problem size of microgenetic algorithms (m-GAs). A discrete time dynamical traffic control problem with different sizes and levels of complexity was used as a test bed. The results showed that upon appropriately sizing the m-GA population, the m-GA can converge to a near-optimal solution in a number of generations equal to the string length. The results also demonstrate that with the selection of appropriate number of generations, it is possible to get most of the worth of the theoretically optimal solution but with only a fraction of the computation cost. The results showed that as the size of the optimization problem grew exponentially, the time requirements of m-GA grew only linearly thus making m-GAs especially suited for optimizing large scale and combinatorial problems for online optimization

Effect of driver & vehicle characteristics on speeds at a tangent section of a rural highway in Jordan

This study investigated the effect of driver and vehicle characteristics on vehicular speeds on a selected tangent rural section of two lane two way highway (speed limit = 80 km/h) in Jordan. The study found that, drivers had an overall average speed of 85.86 km/h with a standard deviation of 17.34 km/h. Older drivers were found to have lower speeds than younger drivers. Male drivers had higher but not significantly different mean speeds than female drivers. Seat belt users had higher mean speeds than non-seat belt users. Considering vehicle type, passenger car were found to have the highest speeds, followed by pickups, busses, vans and trucks respectively. Newer vehicles had higher speeds than older vehicles. Vehicular speeds were inversely proportional to the number of occupants in the vehicle. Loaded pickups and trucks had lower speeds than unloaded ones.