TY - GEN
T1 - Personalized Road Networks Routing with Road Safety Consideration
T2 - IEEE International Smart Cities Conference (ISC2)
AU - Hayes, Sophie
AU - Wang, Shen
AU - Djahel, Soufiene
PY - 2020/10/29
Y1 - 2020/10/29
N2 - Urban road congestion is getting worse with the increasing population and car ownership. Traditional solutions, such as increasing road capacity and dynamic control and adaptation of traffic lights, rely heavily on infrastructure support, which limits their wider adoption and practicality. Vehicle navigation systems, such as Google Maps, TomTom, and AutoNavi, are widely used due to the popularization of smartphones. However, these systems normally provide routes with either shortest travel distance or fastest current travel speed, without any consideration of the drivers' route preferences. For example, the safety level of a road is also very important as it often leads to non-recurring congestion that is more difficult to avoid. In this paper, we propose, implement, and test a personalized routing application that allows end-users to flexibly adjust their route preferences among travel distance, estimated travel time, and the safety level. We present the validation results of our application using a realistic dataset from the city of Manchester in England.
AB - Urban road congestion is getting worse with the increasing population and car ownership. Traditional solutions, such as increasing road capacity and dynamic control and adaptation of traffic lights, rely heavily on infrastructure support, which limits their wider adoption and practicality. Vehicle navigation systems, such as Google Maps, TomTom, and AutoNavi, are widely used due to the popularization of smartphones. However, these systems normally provide routes with either shortest travel distance or fastest current travel speed, without any consideration of the drivers' route preferences. For example, the safety level of a road is also very important as it often leads to non-recurring congestion that is more difficult to avoid. In this paper, we propose, implement, and test a personalized routing application that allows end-users to flexibly adjust their route preferences among travel distance, estimated travel time, and the safety level. We present the validation results of our application using a realistic dataset from the city of Manchester in England.
KW - Vehicle routing
KW - mobile application
KW - personalized routing
KW - road safety
UR - https://pure.hud.ac.uk/en/publications/6485bbac-f7e6-4f3c-8b71-2f52f53bfd9d
UR - http://www.scopus.com/inward/record.url?scp=85097178979&partnerID=8YFLogxK
U2 - 10.1109/ISC251055.2020.9239085
DO - 10.1109/ISC251055.2020.9239085
M3 - Conference proceeding
SN - 9781728182940
SN - 9781728182957
T3 - Proceedings of the IEEE International Smart Cities Conference
SP - 1
EP - 6
BT - 2020 IEEE International Smart Cities Conference, ISC2 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 September 2023 through 27 September 2023
ER -
