Reserve Capacity Model for Optimizing Traffic Signal Timings with an Equity Constraint

This paper represents a solution algorithm for optimizing traffic signal timings in urban road networks by considering reserve capacity with an equity constraint. It is well known that the variation of signal timings in a road network may cause an inequity issue with regard to the travel costs of road users travelling between origin-destination (O-D) pairs. That is, the users may be influenced differently by changing traffic signal timings. In this context, the bilevel programming model is proposed for finding reserve capacity for signalized road networks by taking into account the equity issue. In the upper level, the reserve capacity is maximized with an equity constraint, whereas deterministic user equilibrium problem is dealt in the lower level. In order to solve the proposed model, a heuristic solution algorithm based on harmony search combined with a penalty function approach is developed. The application of the proposed model is illustrated for an example road network taken from a literature

Investigating acceptable level of travel demand before capacity enhancement for signalized urban road networks

Increasing travel demand in urban areas triggers traffic congestion and increases delay in road networks. In this context, local authorities that are responsible for traffic operations seek to strike a balance between traffic volume and capacity to reduce total travel time on road networks. Since signalized intersections are the most critical components of road networks in terms of safety and operational issues, adjusting intersection signal timings becomes an effective method for authorities. When this tool remains incapable of overcoming traffic congestions, authorities take expensive measures such as increasing link capacities, lane additions or applying grade-separated junctions. However, it may be more useful to handle road networks as a whole by investigating the effects of optimizing signal timings of all intersections in the network. Therefore, it would be useful to investigate the right time for capacity enhancement on urban road networks to avoid premature investments considering limited resources of local authorities. In this study, effects of increasing travel demand on Total Travel Cost (TTC) is investigated by developing a bi-level programming model, called TRAvel COst Minimizer (TRACOM), in which the upper level minimizes the TTC subject to the stochastic user equilibrium link flows determined at the lower level. The TRACOM is applied to Allsop and Charlesworths’ network for different common origin-destination demand multipliers. Results revealed that TTC values showed an approximate linear increase while the travel demand is increased up to 16%. After this value, TTC showed a sudden spike although the travel demand was linearly increased that means optimizing signal timings must be supported by applying capacity enhancement countermeasures

Minimization Problems in Signalized Road Networks

In this study, we present a bilevel programming model in which upper level is defined as a biobjective problem and the lower level is considered as a stochastic user equilibrium assignment problem. It is clear that the biobjective problem has two objectives: the first maximizes the reserve capacity whereas the second minimizes performance index of a road network. We use a weighted-sum method to determine the Pareto optimal solutions of the biobjective problem by applying normalization approach for making the objective functions dimensionless. Following, a differential evolution based heuristic solution algorithm is introduced to overcome the problem presented by use of biobjective bilevel programming model. The first numerical test is conducted on two-junction network in order to represent the effect of the weighting on the solution of combined reserve capacity maximization and delay minimization problem. Allsop & Charlesworth's network, which is a widely preferred road network in the literature, is selected for the second numerical application in order to present the applicability of the proposed model on a medium-sized signalized road network. Results support authorities who should usually make a choice between two conflicting issues, namely, reserve capacity maximization and delay minimization. C1 [Baskan, Ozgur; Ceylan, Huseyin] Pamukkale Univ, Dept Civil Engn, Fac Engn, TR-20160 Denizli, Turkey. [Ozan, Cenk] Adnan Menderes Univ, Dept Civil Engn, Fac Engn, TR-09100 Aydin, Turkey. Document type: Articl

Improving the Performance of the Bilevel Solution for the Continuous Network Design Problem

For a long time, many researchers have investigated the continuous network design problem (CNDP) to distribute equitably additional capacity between selected links in a road network, to overcome traffic congestion in urban roads. In addition, CNDP plays a critical role for local authorities in tackling traffic congestion with a limited budget. Due to the mutual interaction between road users and local authorities, CNDP is usually solved using the bilevel modeling technique. The upper level seeks to find the optimal capacity enhancements of selected links, while the lower level is used to solve the traffic assignment problem. In this study, we introduced the enhanced differential evolution algorithm based on multiple improvement strategies (EDEMIS) for solving CNDP. We applied EDEMIS first to a hypothetical network to show its ability in finding the global optimum solution, at least in a small network. Then, we used a 16-link network to reveal the capability of EDEMIS especially in the case of high demand. Finally, we used the Sioux Falls city network to evaluate the performance of EDEMIS according to other solution methods on a medium-sized road network. The results showed that EDEMIS produces better solutions than other considered algorithms, encouraging transportation planners to use it in large-scale road networks.</p

Reinforcement learning approach for optimising traffic signal timings at isolated intersections

One of effective ways to prevent congestion and delay on urban areas is signal control at intersections. Signal systems are operated according to state of intersections either isolated or coordinated signal systems. Many researches have been investigated to improve traffic signal systems based on delay minimization or capacity maximization throughput. Due to complexity of the system, new methods are needed to improve efficiency of signalization in aroad network.Signal setting parameters are usually obtained by minimizing total delay on anintersection. The delay is the key parameter which determines the level of service of an intersection. Delay is defined with two parts as an uniform and non-uniform. The uniform partof the delay is determined basically using conventional delay formulas. But the non-uniformpart is not easily determined and cannot be represent due to the nature of the problem and randomness in arrivals.In this study, Reinforcement Learning Signal Optimizer (RLSO) is used to optimize signal timings in isolated intersection because of reflecting the effect of non-uniform part ofdelay. Reinforcement Learning (RL) which is an approach to artificial intelligence that emphasizes learning by the individual from its interaction with its environment. This contrasts with classical approaches to artificial intelligence and machine learning, which have downplayed learning from interaction, focusing instead on learning from a knowledgeable teacher, or on reasoning from a complete model of the environment. RL is learning what todo-how to map situations to actions-so as to maximize a scalar reward signal. The learner isnot told which action to take, as in most forms of machine learning, but instead must discoverwhich actions yield the most reward by trying them.The aim of this paper is to minimize delay on intersections controlled by isolated signal system and to obtain operational parameters such as cycle time, green split rate. For thispurpose, the RLSO is applied to an example intersection which has four approaches and threestages. The results of RLSO were compared with field observations. The results showed thatthe RLSO is able to optimize traffic signal timings on an intersection. The proposed model also holds promise for successful application to optimize traffic signal timings at isolated intersections according to delay minimization

A Harmony Search Algorithm approach for optimizing traffic signal timings

In this study, a bi-level formulation is presented for solving the Equilibrium Network Design Problem (ENDP). The optimisation of the signal timing has been carried out at the upper-level using the Harmony Search Algorithm (HSA), whilst the traffic assignment has been carried out through the Path Flow Estimator (PFE) at the lower level. The results of HSA have been first compared with those obtained using the Genetic Algorithm, and the Hill Climbing on a two-junction network for a fixed set of link flows. Secondly, the HSA with PFE has been applied to the medium-sized network to show the applicability of the proposed algorithm in solving the ENDP. Additionally, in order to test the sensitivity of perceived travel time error, we have used the HSA with PFE with various level of perceived travel time. The results showed that the proposed method is quite simple and efficient in solving the ENDP

Using Potential Accessibility Measure for Urban Public Transportation Planning: A Case Study of Denizli, Turkey

Policy makers and planners evaluate the implementation of the urban public transport (UPT) planning studies in terms of some objective measures such as load factor, mean volume per trip, capacity usage ratio and total capacity. In some cases, improving these measures may lead an unforeseen decrease on accessibility to the opportunities in terms of UPT users. Thus, this study aims to evaluate Potential Accessibility (PA) as an efficiency measure in decision stage of UPT planning. It widely depends on fieldwork, surveys, data inventories and existing plans. In this context, a comprehensive UPT planning has been carried out through VISUM traffic simulation software by taking the PA into account, and a four-step UPT planning procedure has been proposed. The results showed that PA may alternatively be used as an evaluation instrument in decision stage of UPT planning while the objective measures are insufficient to represent the effectiveness of alternative scenarios