Effectiveness of en route traffic information in developing countries using conventional discrete choice and neural‐network models

Abstract

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.

A heuristic methodology to tackle the Braess Paradox detecting problem tailored for real road networks

“…The convexity of the LoS index complies with the results of LoS analysis in Chen et al (2002). More convexity implies that marginal impact of change in travel time is felt much strongly in short trips compared with long trips (Bagloee, Ceder, and Bozic 2013;Chen et al 2002).…”

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