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|Title:||Predicting the duration and impact of the non-recurring road incidents on the transportation network||Authors:||Ghosh, Banishree||Keywords:||DRNTU::Engineering::Civil engineering::Transportation||Issue Date:||29-May-2019||Source:||Ghosh, B. (2019). Predicting the duration and impact of the non-recurring road incidents on the transportation network. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||Non-recurring incidents such as accidents, vehicle breakdowns, etc. are leading causes of severe traffic congestion in large cities. Consequently, anticipating the impact of such events in advance can be highly useful in mitigating the resultant congestion. However, availability of partial information or ever-changing ground conditions makes the task of forecasting the impact particularly challenging. In this thesis, we propose adaptive ensemble models that can provide reasonable forecasts even when a limited amount of information is available and further improves the prediction accuracy as more information becomes available during the course of the incidents. Furthermore, we consider the scenarios where the historical incident reports may not always contain accurate or complete information about the duration and length of congestion due to the incidents. To mitigate this issue, we fi rst quantify the effective duration and queue-length of the incidents by looking for the change points in traffic state (average traffic speed and flow data) of the individual upstream links and then utilize this information to predict the duration and queue-length of the incidents. The prediction models forecasts the values continually with elapsed time until the end of the incidents to comprehend the temporal dynamicity. We compare the performance of different traditional regression methods in predicting the duration, and the experimental results show that the Treebagger outperforms other methods. The overall MAPE value averaged over all incidents improves by 50% with elapsed time. On the other hand, we build a queue-length prediction model using a Long Short-Term Memory neural network which incorporates the updated traffic data and various spatio-temporal features as inputs. At the start of the incident, the proposed model has a mean error value of 73.7%, which reduces to 45.6% after one hour of prediction. Moreover, we are interested in not only predicting incidents' impact but informing the drivers about the current and future traffic state is also our area of concern. Currently, in order to inform or alert the commuters about the traffic situation during the road incidents, several LED displays, better known as variable message signs or VMS messages, have been installed by the LTA on the expressways of Singapore. Therefore, apart from building the predicting models, this thesis also aims to evaluate the impact of VMS displays on the overall traffic distribution of Singapore whether these displays are really helpful to the drivers or not. To this end, the incidents data and their corresponding VMS messages are collected from the two busiest expressways of Singapore, namely Pan Island Expressway (PIE) and Central Expressway (CTE). The analysis shows that approximately 14% of the vehicles change their direction after the VMS messages have been activated. Lastly, since Singapore is a tropical country having a significant amount of rainfall throughout the entire year, the weather condition has a noticeable impact on the traffic of Singapore. Therefore, we also aim to analyze the influence of rainfall on traffic incidents if the frequency of these incidents, especially accidents, increases after rainfall or not. Therefore, the rainfall data acquired from the National Environmental Agency (NEA) of Singapore is analyzed to investigate the correlation between the occurrence of traffic incidents and rainfall. Overall, the obtained results support the hypothesis that the frequency of traffic incidents is higher during rainfall as compared to dry periods. Moreover, the frequency is the highest after rainfall. Besides, it is observed that traffic speed and flow decrease by 10.14% and 3.88% respectively during rainy weather. Overall, this thesis aims to help avoid incident-induced traffic jams by designing the urban traffic prediction models. Thus, the cost of time and fuel can be saved, which will benefi t the national economy as a whole.||URI:||https://hdl.handle.net/10356/90152
|DOI:||https://doi.org/10.32657/10220/48435||Fulltext Permission:||embargo_20200531||Fulltext Availability:||With Fulltext|
|Appears in Collections:||IGS Theses|
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