Incorporating weather variables with probabilistic approach for trip planning

Adverse weather conditions are known to negatively impact traffic operations by creating unsafe road conditions, slowing vehicles, and causing congestion - events that subsequently lead to extra costs and travel time delay if they are not considered explicitly in trip planning. This study proposes an approach that considers incorporates adverse weather conditions into trip planning modeling. As such, speed data from Weigh-in-Motion (WIM) sensors along with weather information from multi-region within the State of Ohio were used. To assess departure time and route choice under varying levels of snowstorm events, a probabilistic model was developed. Travel time probability is used to quantify the impact of weather events on travelers' decisions regarding both route choice and departure time. As expected, the results showed that heavy snow associated with low visibility has the most impact on driver speed behavior. A measure of driver's speed variability associated with different weather conditions is provided for the collected data from the studied regions in Ohio. Moreover, some drivers showed hesitation during the storm and dropped their speed to as low as 20 mph. In addition, the vehicle speed performance during the events was used to calculate the expected time throughout each event. Simulations on a road network were used to compare the expected total travel time and the variance along with the probability of paths under a heavy snowstorm. Results indicated that, under adverse weather conditions, drivers' mute choice is not based on the shortest travel time but on the decision of alternate routes being attractive (safe, snow cleared, etc.). Giving the additional trip information of these events, decision-makers should pay more attention before deciding the departure and route choice on heavy snow events.