Gabriel B. Kalejaiye
Henrique R. Orefice
Marcelo M. Carvalho
ABSTRACT
The design of crowd sensing applications that can supplement public transportation information systems have generally assumed availability of high-speed Internet connections coupled with high data sampling and gathering via data-hungry application interfaces. But, in developing regions, low-income users generally avoid the use of data-intensive applications over the Internet connection provided by their mobile operator. Moreover, transit centers and bus operators in such regions are generally poorly equipped or incapable of providing any infrastructure support. Based on this fact, this paper presents the system requirements and system concept of a mobile application that is being developed for the problem of bus arrival time prediction in developing regions. The proposed application seeks minimal data exchange with each user, by gathering data only when the user is static, standing at a bus stop.
- James Biagioni, Tomas Gerlich, Timothy Merrifield, and Jakob Eriksson. 2011. EasyTracker: automatic transit tracking, mapping, and arrival time prediction using smartphones. In 9th Int. Conf. on Embedded Networked Sensor Systems. ACM. Google Scholar
Digital Library
- Luis G. Jaimes, Idalides J. Vergara-Laurens, and Andrew Raij. 2015. A Survey of Incentive Techniques for Mobile Crowd Sensing. IEEE Internet of Things Journal 2, 5 (2015), 370--380. Google Scholar
- T. Moran and S. Wang. 2007. School bus tracking and notification system. (Feb. 1 2007). US Patent App. 11/193,544.Google Scholar
- Rajat Rajbhandari. 2005. Bus Arrival Time Prediction Using Stochastic Time Series and Markov Chains. Ph.D. Dissertation. New Jersey Institute of Technology.Google Scholar
- Rohit Verma, Aviral Shrivastava, Bivas Mitra, Sujoy Saha, Niloy Ganguly, Subrata Nandi, and Sandip Chakraborty. 2016. UrbanEye: An Outdoor Localization System for Public Transport. In Proc. IEEE INFOCOM. 1--9.Google ScholarCross Ref
- Lei Wang, Zhongyi Zuo, and Junhao Fu. 2014. Bus Arrival Time Prediction Using RBF Neural Networks Adjusted by Online Data. Procedia -- Social and Behavioral Sciences 138 (2014), 67--75. Google ScholarCross Ref
Recommendations
Social Sensing in Developing Regions: Challenges for Bus Arrival Time Prediction
SocialSens'17: Proceedings of the 2nd International Workshop on Social SensingThe design of crowdsourcing applications to supplement public transportation information systems have generally assumed availability of high-speed Internet connection coupled with high data sampling and gathering via data-hungry application interfaces. ...
Bus arrival time prediction with real-time and historic data
Provision of accurate bus arrival information is vital to passengers for reducing their anxieties and waiting times at bus stop. GPS-equipped buses can be regarded as mobile sensors probing traffic flows on road surfaces. In this paper, we present an ...
Performance prediction of a discrete-time batch arrival retrial queue with Bernoulli feedback
This paper examines a discrete-time GeoX/G/1 retrial queue wherein the customer is feedback again to the head of the queue with some probability in case when he is unsatisfied with his service. This phenomenon is called as Bernoulli feedback. We ...
Comments