Michigan State University
Department of Civil and Environmental Engineering
SPARTRANS Seminar Series
Thursday, October 10, 2019
11:00 am – 12:00 pm EDT
3540 Engineering Building
Active Connected Work Zone Variable Speed Warning System
Fatal crashes in work zones have recently increased, including those crashes where speeding was identified as
a contributing factor. Studies show that static signs indicating reduced speed limits in work zones may not be
effective in reducing speeds. This presentation details a proposed solution that provides advance warning of
work zones through a variable speed advisory message and an active and connected display system. A switch
triggers an intelligent warning system via Dedicated Short-Range Communication (DSRC) to roadside units,
which communicate with upstream dynamic message signs and approaching vehicles. The connected DMS is
programmed to display two sets of three-line dynamic message showing based on presence of workers. This
system provides real-time information of worker presence to drivers enabling drivers to adjust their speed in
accordance with actual work zone condition (i.e., presence or absence of workers). The warning system
increases trust in the speed regulation and, thus, improves the safety of workers in the work zone.
Nusayba Megat-Johari is a PhD student in the Department of Civil and Environmental
Engineering at MSU. During her professional career, Nusayba has been involved in road safety
research and interventions in Malaysia relating to vulnerable road users, particularly school
children and motorcyclists. She has experience in conducting over 30 road safety assessments
as part of the Malaysian Institute of Road Safety Research operation works and has contributed
safety recommendations and interventions to local highway and road authorities.
A Learning-based Network Contraction Approach for Optimal Path Finding Problem
Path finding problem has a broad application in different fields of engineering. Travel time uncertainty is a
critical factor affecting this problem and the route choice of transportation users. The major downside of the
existing algorithms for the reliable path finding problem is their inefficiency in computational time. This study
aims to develop a network contraction approach to reduce the network size of each specific origin and
destination (OD) pair in stochastic time-dependent networks. The network contraction is based on the
comparison of optimistic and pessimistic solutions resulting from minimum and maximum travel time
realizations of a Monte-Carlo simulation-based (MCS) approach. In this respect, the researchers propose a
learning approach to utilize the information of the realizations in the initial iterations of the MCS approach.
Implementation of this approach is in place for several OD pairs of two real-world large-scale applications. The
results demonstrate significant computational improvements, with an acceptable accuracy level relative to the
approach without network contraction.
Fatemeh Fakhrmoosavi is a PhD student and graduate research assistant in the Department of
Civil and Environmental Engineering at MSU. Her research interests mainly focus on dynamic
network modeling and urban transportation planning. Her research incorporates travel time
variability and reliability valuation of heterogeneous users into different stages of dynamic traffic
assignment. She is currently engaged in multiple research projects including electric vehicle
charger placement optimization project in Michigan.