Name
Abstract | ||
|---|---|---|
Identification of Public Bus Stops and characterising their waiting time is of significance in the domain of Intelligent Transportation System and public interest. Surveys suggest that the existing systems and algorithms cannot be merely replicated in the context of developing regions, because of the kind of heterogeneity and chaotic situation prevalent in their transport system, in contrast to the orderly and much regulated system of the developed world. Moreover a significant fraction of population residing here is not so tech-savvy, hence crowd-sourced based strategies renders useless. Either novel algorithms/techniques are required or the existing ones have to undergo suitable adaptation and innovation to suit the contrasting characteristics of developing regions. Using customized hardware we present the stoppage pattern analysis of public bus GPS traces of more than 2000 km for a 20 km route of Durgapur, a suburban city (West Bengal, India). We have revealed that 53.03 % of the travel time for the selected route has higher predictability with Standard deviation of 30.73%. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/PERCOMW.2015.7134046 | Pervasive Computing and Communication Workshops |
Keywords | Field | DocType |
Global Positioning System,cartography,intelligent transportation systems,traffic information systems,Durgapur,Google Map Transit,ITS,India,West Bengal,crowd-sourced based strategies,developing regions,intelligent transportation system,population fraction,public bus GPS traces,public bus stops identification,stoppage pattern analysis,suburban city,transportation information service,travel time | Population,Predictability,Telecommunications,Algorithm design,Public interest,Computer science,Transport engineering,Computer network,Global Positioning System,Developing regions,Intelligent transportation system,Cluster analysis | Conference |
Citations | PageRank | References |
2 | 0.47 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mandal, R. | 1 | 2 | 0.47 |
| Nitin Agarwal | 2 | 629 | 62.97 |
| Subrata Nandi | 3 | 71 | 21.37 |
| Das, P. | 4 | 14 | 1.86 |