## 3 Data Collection  
Currently, Dubai has various modes of transportation available including; auto; metro; bus; tram; truck; walk (outdoor and indoor); bike; ferry and boat modes. (Road and Transport Authority of Dubai; [[https://traffic.rta.ae/trfersrv/public_resources/service-catalogue.do](https://traffic.rta.ae/trfersrv/public_resources/service-catalogue.do)]([https://traffic.rta.ae/trfersrv/public_resources/service-catalogue.do](https://traffic.rta.ae/trfersrv/public_resources/service-catalogue.do))). Ideally speaking, if all of these modes can be reliably estimated, many ITS applications can benefit from the results in the future. Figure 1 shows all currently-available modes of transportation in Dubai. This paper first develops a universal mode detection ANN that classifies the multiple modes of transportation. Then, a specific ITS application of traffic monitoring based on private vehicles, known as an auto mode, with ANNs is developed which can be useful, especially for transportation practitioners. Conventional GPS data loggers (GL-750 FL by Canmore Electronics Co. LTD, Hsinchu City, Taiwan, [[http://www.canmore.com.tw/product.php](http://www.canmore.com.tw/product.php)]([http://www.canmore.com.tw/product.php](http://www.canmore.com.tw/product.php))) and smartphones (Galaxy S8 by Samsung Electronics, Seoul, South Korea, with GPS, accelerometer, and magnetometer sensors, [[http://www.samsung.com/global/galaxy/galaxy-s8/](http://www.samsung.com/global/galaxy/galaxy-s8/)]([http://www.samsung.com/global/galaxy/galaxy-s8/](http://www.samsung.com/global/galaxy/galaxy-s8/))) are dispatched along major routes of Dubai on different modes of transportation: auto, metro, bus, tram, truck, walk, bike, and water transit vehicles (ferries and boats). The devices have been attached on either side of the waist belt of data collection participants. The sampling rate is set at 1 Hz (once per second) for both devices. In the spring of 2017, in total, 110 weekday hours of data are collected including morning peak (7-9 a.m.), afternoon peak (5-7 p.m.), and non-peak periods (10 a.m.-2 p.m.). The morning peak, afternoon peak, and non-peak periods account for 25%, 25%, and 50% of all data, respectively. Sixty percent of the data are used to train the ANNs and 40% of the data are used as unseen data for evaluating the performance of the developed ANNs. The ratio between the training and validating data are arbitrarily chosen in such a way that the training phase is given with more data for building the ANNs. Analyses on the effect of  
Figure 1: Available modes of transportation in Dubai. varying ratios between the two datasets are beyond the scope of this paper. Ten people in total with 10 GPS data loggers and 10 smartphones have collected similar amounts of data from different modes of transportation. The following criteria are considered for choosing routes and time slots for the data collection in Dubai. 1. Representative routes of Dubai should share as many modes as possible in order for the different modes to experience similar physical surrounding conditions nearby so that the proposed ANN's distinguishing power can focus on the characteristic variances among the different modes. 2. The data should be collected from different types of roads (highways, arterial roads) from different regions of the city that would fairly represent the overall road conditions throughout the city. 3. The data should be collected from both peak periods (rush hour) and non-peak periods (non-rush hour) because the traffic conditions are typically different between those time periods. Figure 2 shows the study area with different routes for different modes. For the auto mode, major highways (E11, E44, and D63) and a loop of nearby arterial roads (318 th, 8 th, Al Thanya St., Al Marabea Rd., Al Waha St., and Umm Al Sheif Rd.) are chosen. For the metro mode, which operates on an elevated rail that is about 20 m above the ground, a section between Nakheel station and Al Rigga station is selected.