Abstract

It is very complex to assure the dynamics of traffic networkin urban environment because of the intensity of traffic flowsand the limited space in the urban area. Signalized intersectionsare undoubtedly very important and frequent element inroad traffic network in urban regions, which greatly influencesthe traffic flow dynamics on each section as in the whole trafficnetwork. The time (delay) a vehicle needs to drive through anintersection directly influences the travel comfort, fuel consumption,traffic pollution and so on. There are many methodsfor dimensioning signalized intersections in the world literature.One of the most applied methods is the HIGHWAY CAPACI1YMANUAL (HCM) 2000 which is based on competentinput traffic flows and other characteristics of traffic signalregulations. The intention of this paper is to establish whetherwe can use the HCM 2000 method directly in the Slovenian realenvironment. Based on the established methodology of researchand limited number of delay measurement in real environmentintersections, there have been some deviations. Thecompletion of the level of service criterion for signalized intersectionshas been proposed, with the intention for more suitabilityestimation in the realistic environment. Zagotoviti dinamično odvijanje prometa na urbanem cestnem omrežju je vsled intenzitete prometnih tokov in omejenega prostora v mestih zelo kompleksno. Križišča s svetlobno signalnimi napravami so nedvomno pomemben dejavnik, ki pogojuje funkcionalnost omrežja. Čas, ki ga vozilo potrebuje za prevoz križišča neposredno vpliva na udobnost potovanja, porabo goriva in posledično na onesnaževanje okolja. V svetovni literaturi je zaslediti dosti metod za dimenzioniranje semaforiziranih križišč. Ena najpogostejše uporabljenih metod je HCM 2000, ki temelji na merodajnih vhodnih prometnih tokovih ter drugih značilnosti tovrstnih prometnih ureditev. Namen članka je preveriti, ali je moč predlagano metodo direktno uporabiti v slovenskem realnem okolju. Na osnovi vzpostavljene metodologije raziskovanja in omejenemu številu meritev zamud vsled signalnih naprav v križišču, so bila ugotovljena določena odstopanja. Predlagane so dopolnitve kriterija za določitev nivoja usluge za križišča s svetlobno signalnimi napravami, ki ustrezneje opredeljujejo dejansko stanje v realnem prometnem prostoru.

Abstract

fuzzy model is proposed here to speed up traffic flows and reduce delay at road-signalized intersections by using real-time measures and traffic lights adaptation. The proposed demand-responsive control approach combines fixed-duration cycle and fuzzy calculus to reduce the overall delay without giving too much penalty to traffic streams. The traffic lights signal modification is based on the concept of “intersection saturation degree” that identifies the intersection performances. The numerical results of the proposed fuzzy approach confirm its better performances with respect to the other traditional control approaches.

Abstract

Modeling vehicle delay has been an interesting subject for traffic engineers and urban planners. Determination of vehicle delay is a complex task and the delay is influenced by many variables that have uncertainties and vagueness, especially for non-uniform or over-saturated conditions. In this study, vehicle delay is modeled using new approaches such as Fuzzy Logic (FL) and Artificial Neural Networks (ANN) to deal with all conditions. The Neuro Fuzzy Delay Estimation (NFDE) model and Artificial Neural Networks Delay Estimation (ANNDE) model are developed. The overall delay data required for the model were collected from ten signalized intersections in Turkey. The results of the developed models are compared with the Highway Capacity Manual (HCM), Akqelik's methods and the delay data collected from intersections. The results showed that delay estimations by the ANNDE and NFDE model are promising. It is also inferred that the NFDE model results are the best fitted. The Average Relative Error (ARE) rates of NFDE model are determined as 7% for under-saturated and 5% for over-saturated conditions. The results reflect the fact that the neuro-fuzzy approach may be used as a promising method in vehicle delay estimation. (c) 2006 Elsevier Ltd. All rights reserved. C1 Pamukkale Univ, Fac Engn, Dept Civil Engn, TR-20070 Kinikli, Denizli, Turkey.

Abstract

urban intersections drivers handle multiple tasks simultaneously, making urban driving a complex task. An advanced driver assistance system may support drivers in this specific driving task, but the design details of such a system need to be determined before they can be fully deployed. A driving simulator experiment was conducted to determine the relationship between different subtasks of driving at urban intersections. Participants completed four drives, each comprising 20 comparable intersections with different traffic situations and encountered one unexpected braking event during the experiment. The effects of varying levels of event urgency on the relationship between different driving subtasks were studied. Furthermore, the influence of workload on this relationship was determined by giving half of the subjects an additional cognitive task. After the lead car braked unexpectedly, participants reduced speed and increased headway depending on the urgency of the braking event. Depending on the workload, participants returned to the normal speed and headway again after a number of intersections. Participants experiencing a high-workload drove more smoothly, except for those who had experienced the most urgent unexpected event. High workload additionally affected the length of the adjustments to the unexpected event.

Abstract

The transportation demand is rapidly growing in metropolises, resulting in chronic traffic congestions in dense downtown areas. Adaptive traffic signal control as the principle part of intelligent transportation systems has a primary role to effectively reduce traffic congestion by making a real-time adaptation in response to the changing traffic network dynamics. Reinforcement learning (RL) is an effective approach in machine learning that has been applied for designing adaptive traffic signal controllers. One of the most efficient and robust type of RL algorithms are continuous state actor-critic algorithms that have the advantage of fast learning and the ability to generalize to new and unseen traffic conditions. These algorithms are utilized in this paper to design adaptive traffic signal controllers called actor-critic adaptive traffic signal controllers (A-CATs controllers). The contribution of the present work rests on the integration of three threads: (a) showing performance comparisons of both discrete and continuous A-CATs controllers in a traffic network with recurring congestion (24-h traffic demand) in the upper downtown core of Tehran city, (b) analyzing the effects of different traffic disruptions including opportunistic pedestrians crossing, parking lane, non-recurring congestion, and different levels of sensor noise on the performance of A-CATS controllers, and (c) comparing the performance of different function approximators (tile coding and radial basis function) on the learning of A-CATs controllers. To this end, first an agent-based traffic simulation of the study area is carried out. Then six different scenarios are conducted to find the best A-CATs controller that is robust enough against different traffic disruptions. We observe that the A-CATs controller based on radial basis function networks (RBF (5)) outperforms others. This controller is benchmarked against controllers of discrete state Q-learning, Bayesian Q-learning, fixed time and actuated controllers; and the results reveal that it consistently outperforms them.