Adding a new road to help traffic flow in a congested urban network may at first appear to be a good idea. The Braess Paradox (BP) says, adding new capacity may actually worsen traffic flow. BP does not only call for extra vigilance in expanding a network, it also highlights a question: Does BP exist in existing networks? Literature reveals that BP is rife in real world. This study proposes a methodology to find a set of roads in a real network, whose closure improve traffic flow. It is called the Braess Paradox Detection (BPD) problem. Literature proves that the BPD problem is highly intractable especially in real networks and no efficient method has been introduced. We developed a heuristic methodology based on a Genetic Algorithm to tackle BPD problem. First, a set of likely Braess-tainted roads is identified by simply testing their closure (one-by-one). Secondly, a seraph algorithm is devised to run over the Braess-tainted roads to find a combination whose closure improves traffic flow. In our methodology, the extent of road closure is limited to some certain level to preserve connectivity of the network. The efficiency and applicability of the methodology are demonstrated using the benchmark Hagstrom-Abrams network, and on a network of city of Winnipeg in Canada.

Providing commuters with traffic information or advising them of alternative routes during traffic incidents can alleviate congestion. For decades, advanced traveler information services (ATIS) have been devised and dedicated to this role. ATIS comprises a wide variety of technologies across the world, including radio traffic information (RTI) advisory service. RTI is common in both developed and developing countries. Although extensive literature and evaluation results of ATISs and RTI are available in developed countries, little attention has been devoted to that in developing countries. This work provides a modeling platform to study drivers' response to en route traffic information provided by Radio-Payam broadcasting service in Tehran, the capital city of the developing country of Iran. The results are compared with counterpart cases in developed countries. Past studies and this study have employed conventional discrete models for drivers' response, such as ordered logit and ordered probit. This work evaluates the accuracy level of these conventional models in comparison with a developed neural-network (NN) model, because it has been widely proven that NN models are highly precise. It has also been found that, apart from reliability, the conventional models are within an acceptable level of prediction accuracy compared with the NN models. The results show a high degree of similarities between the case of Tehran and its counterparts in the developing countries. The results also deliver some insights on how to improve or better implement the ATIS technologies.The survey's information can be classified into three classes: (i) interviewee's personal information: age, gender, job, education level and marriage status; (ii) job-related information: travel time and length from home to work place, working time and PAT at work; and (iii) driver's behavior information in which tendency for diversion, propensity to tuning to the RTI, familiarity with alternative routes, and so on, had also been reported by the interviewees. The cleaned survey records resulted in a database of 376 records.As discussed earlier, the dependent variable to be modeled is the answer to the question Q9 in the questionnaire in which the drivers are asked how often they do en route diversion. Possible answers are 1 = never, 2 = rarely, 3 = sometimes and 4 = very often. Figure 3. Questionnaire used for the survey.

No abstract

No abstract

Making efficient use of transportation infrastructure has always been a concern. Traveler information provisions (TIPs) assist commuters in making better travel decisions and therefore lead to increased mobility and more efficient travel. The ongoing economic crisis and shrinking public funding have made the public–private partnership (PPP) an at tractive instrument for promoting TIP. Such initiatives have been much discussed in developed countries. The use of PPP to fund public projects in developing countries, however, is in its infancy, and the related issues do not receive adequate attention in the literature. In this study, PPP opportunities in TIP were assessed in the case of a city in a developing country, Tehran, Iran. Factors contributing to TIP effectiveness were studied by analyzing commuters’ behavioral changes in response to current radio traffic information. Analysis was conducted with multinomial logit and nested logit models. From this analysis, commuter characteristics, trip purpose, and content of radio traffic information were identified as factors contributing to drivers’ willingness to divert. These findings directed the research to some potential markets and strategies for investment in TIPs. The literature was reviewed to identify some challenges and opportunities of PPP in past experience as well as special regional constraints. On the basis of the modeling results and literature review, business models for PPP in TIPs were outlined. Key findings are that (a) data integration and customization are essential; (b) with respect to purchasing behavior, the market has to be stratified into commuter and corporate markets; and (c) the market for Global Positioning System–based services is promising.