Recent research has focused on the safety or mobility impacts of signal timing. Several studies have compared the choice between a protected-only left turn (PO) and a protected-permissive left turn (PPLT). However, few have compared both the safety and mobility impacts, and their tradeoffs. This study proposed data-driven methods to conduct a pilot study at an intersection in Tucson, Arizona. This study evaluated the impacts on vehicular mobility and multi-modal safety when changing from a PPLT to a PO. First, the daily and annual delay for the through and left-turn movements for the intersection was evaluated using a calibrated delay model and year-long 15-min traffic sensor data. Then, real-world near misses between cyclists, pedestrians, and vehicles were manually collected and analyzed using 48 h of videos. Last, both mobility and safety measures were converted into an annual cost to determine the trade-off between the before (PPLT) and the after (PO) situations. The results of this study demonstrate the feasibility of the proposed methods, providing practitioners with different options to evaluate left-turn phasing strategies effectively and efficiently.

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Trip purpose information plays a significant role in transportation systems. Existing trip purpose information is traditionally collected through human observation. This manual process requires many personnel and a large amount of resources. Because of this high cost, automated trip purpose estimation is more attractive from a data-driven perspective, as it could improve the efficiency of processes and save time. Therefore, a hybrid-data approach using taxi operations data and point-of-interest (POI) data to estimate trip purposes was developed in this research. POI data, an emerging data source, was incorporated because it provides a wealth of additional information for trip purpose estimation. POI data, an open dataset, has the added benefit of being readily accessible from online platforms. Several techniques were developed and compared to incorporate this POI data into the hybrid-data approach to achieve a high level of accuracy. To evaluate the performance of the approach, data from Chengdu, China, were used. The results show that the incorporation of POI information increases the average accuracy of trip purpose estimation by 28% compared with trip purpose estimation not using the POI data. These results indicate that the additional trip attributes provided by POI data can increase the accuracy of trip purpose estimation.

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