Despite the significant advances that have been made in traffic sensor technologies, there are only a few systems that provide measurements at the trip level and fewer yet that can do so for all travel modes. On the other hand, traditional methods of collecting individual travel behavior (i.e. manual or web-based travel diaries) are resource intensive and prone to a wide range of errors. Moreover, although dedicated GPS loggers provide the ability to collect detailed travel behavior data with less effort, their use still faces several challenges including the need to distribute and retrieve the logger; the potential need to have the survey participants upload data from the logger to a server; and the need for survey participants to carry another device with them on all their trips. The widespread adoption of smart phones provides an opportunity to acquire travel behavior data from individuals without the need for participants to record trips in a travel diary or to carry dedicated recording devices with them on their travels. The collected travel data can then be used by municipalities and regions for forecasting the travel demand or for analyzing the travel behavior of individuals. In the current research, a smart phone based travel behavior surveying system is designed, developed, and pilot tested. The custom software written for this study is capable of recording the travel characteristics of individuals over the course of any period of time (e.g. days or weeks) and across all travel modes. In this system, a custom application on the smart phone records the GPS data (using the onboard GPS unit) at a prescribed frequency and then automatically transmits the data to a dedicated server. In the server, the data are stored in a dedicated database to be then processed using trip characteristics inference algorithms. The main challenge with the implemented system is the need to reduce the amount of energy consumed by the device to calculate and transmit the GPS fixes. In order to reduce the power consumption from the travel behavior data acquisition software, several techniques are proposed in the current study. Finally, in order to evaluate the performance of the developed system, first the accuracy of the position information obtained from the data acquisition software is analyzed, and then the impact of the proposed methods for reducing the battery consumption is examined. As a conclusion, the results of implemented system shows that collecting individual travel behavior data through the use of GPS enabled smart phones is technically feasible and would address most of the limitations associated with other survey techniques. According to the results, the accuracy of the GPS positions and speed collected through the implemented system is comparable to GPS loggers. Moreover, proposed battery reduction techniques are able to reduce the battery consumption rate from 13.3% per hour to 5.75% per hour (i.e. 57% reduction) when the trip maker is non-stationary and from 5.75% per hour to 1.41% per hour (i.e. 75.5% reduction) when the trip maker is stationary.

Despite the significant advances that have been made in traffic sensor technologies, there are only a few systems that provide measurements at the trip level and fewer yet that can do so for all travel modes. On the other hand, traditional methods of collecting individual travel behavior (i.e. manual or web-based travel diaries) are resource intensive and prone to a wide range of errors. Moreover, although dedicated GPS loggers provide the ability to collect detailed travel behavior data with less effort, their use still faces several challenges including the need to distribute and retrieve the logger; the potential need to have the survey participants upload data from the logger to a server; and the need for survey participants to carry another device with them on all their trips. The widespread adoption of smart phones provides an opportunity to acquire travel behavior data from individuals without the need for participants to record trips in a travel diary or to carry dedicated recording devices with them on their travels. The collected travel data can then be used by municipalities and regions for forecasting the travel demand or for analyzing the travel behavior of individuals. In the current research, a smart phone based travel behavior surveying system is designed, developed, and pilot tested. The custom software written for this study is capable of recording the travel characteristics of individuals over the course of any period of time (e.g. days or weeks) and across all travel modes. In this system, a custom application on the smart phone records the GPS data (using the onboard GPS unit) at a prescribed frequency and then automatically transmits the data to a dedicated server. In the server, the data are stored in a dedicated database to be then processed using trip characteristics inference algorithms. The main challenge with the implemented system is the need to reduce the amount of energy consumed by the device to calculate and transmit the GPS fixes. In order to reduce the power consumption from the travel behavior data acquisition software, several techniques are proposed in the current study. Finally, in order to evaluate the performance of the developed system, first the accuracy of the position information obtained from the data acquisition software is analyzed, and then the impact of the proposed methods for reducing the battery consumption is examined. As a conclusion, the results of implemented system shows that collecting individual travel behavior data through the use of GPS enabled smart phones is technically feasible and would address most of the limitations associated with other survey techniques. According to the results, the accuracy of the GPS positions and speed collected through the implemented system is comparable to GPS loggers. Moreover, proposed battery reduction techniques are able to reduce the battery consumption rate from 13.3% per hour to 5.75% per hour (i.e. 57% reduction) when the trip maker is non-stationary and from 5.75% per hour to 1.41% per hour (i.e. 75.5% reduction) when the trip maker is stationary.