Analytical Model to Estimate Ride Pooling Traffic Impacts by Using the Macroscopic Fundamental Diagram

Ride pooling services are considered as a customer-centric mode of transportation, but, at the same time, an environmentally friendly one, because of the expected positive impacts on traffic congestion. This paper presents an analytical model that can estimate the traffic impacts of ride pooling on a city by using a previously developed shareability model, which captures the percentage of shared trips in an area, and the existence of a macroscopic fundamental diagram for the network of consideration. Moreover, the analytical model presented also investigates the impact that improving the average velocity of a city has on further increasing the percentage of shared trips in an operation area. The model is validated by means of microscopic traffic simulations for a ride pooling service operating in the city of Munich, Germany, where private vehicle trips are substituted with pooled vehicle trips for different penetration rates of the service. The results show that the average velocity in the city can be increased by up to 20% for the scenario when all private vehicle trips are substituted with pooled vehicle trips; however, the improvement is lower for smaller penetration rates of ride pooling. The operators and cities can use this study to quickly estimate the traffic impacts of introducing a ride pooling service in a certain area and for a certain set of service quality parameters.

Evaluating Traffic Impacts of Permitting Trucks in Transit-Only Lanes

With ongoing population growth and rapid development in cities, the demand for goods and services has seen a drastic increase. Consequently, transportation planners are searching for new ways to better manage the flow of traffic on existing facilities, and more efficiently utilize available and unused capacity. In this research, a lane management strategy that allows freight vehicles to use bus-only lanes is empirically evaluated in an urban setting. This paper presents an analysis of data that was collected to evaluate the operational impacts of the implementation of a freight and transit (FAT) lane, and to guide the development of future FAT lane projects by learning from the case study in Seattle, U.S. The video data was converted to vehicle counts, which were analyzed to understand the traffic impacts and used to construct a discrete choice model. The analysis shows that transit buses used the FAT lane 96% of the time, and authorizing trucks to use the lane did not affect that lane choice. Trucks used the FAT lane, but their utilization decreased with increasing numbers of buses in the FAT lane. Instead of higher rates of trucks, unauthorized vehicles, such as passenger cars and work vans, increasingly used the FAT lane during congestion. As a result of their differing schedule patterns, trucks and buses used the FAT lane at complementary times and trucks showed relatively low volumes in the FAT lane. Overall, the results are promising for a lane management strategy that may improve freight system performance without reducing transit service quality.

From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model

The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO2 and particulate matter with aerodynamic diameters <2.5 μm by –30.1% and –17.5%, respectively, but a 5.7% increase in O3. Heavy-duty truck emissions contribute primarily to these variations. Future traffic-emission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO2 levels.

Spatiotemporal Analysis of Highway Traffic Patterns in Hurricane Irma Evacuation

The State of Florida is significantly vulnerable to catastrophic hurricanes that cause widespread infrastructural damage and claim lives annually. In 2017, Hurricane Irma, a Category 4 hurricane, took on the entirety of Florida, causing the state’s largest evacuation ever as 7 million residents fled the hurricane. Floridians fleeing the hurricane faced the unique challenge of where to go, since Irma made an unusual landfall from the south, enveloping the entire state, forcing evacuees to drive farther north, and creating traffic jams along Florida’s evacuation routes that were worse than during any other hurricane in Florida's history. This study aimed to assess the spatiotemporal traffic impacts of Irma on Florida’s major highways based on real-time traffic data before, during, and after the hurricane made landfall. First, we conducted a time-series-based analysis to evaluate the temporal evacuation patterns of this large-scale evacuation. Second, we developed a metric, namely the congestion index (CI), to assess the spatiotemporal evacuation patterns on I-95, I-75, I-10, I-4, and turnpike (SR-91) highways with a focus on both evacuation and returning traffic. Third, we employed a geographic information system-based analysis to visually illustrate the CI values of corresponding highway sections with respect to different dates and times. Findings clearly showed that imperfect forecasts and the uncertainty surrounding Irma’s predicted path resulted in high levels of congestion and severe delays on Florida’s major evacuation routes.

Analyzing Traffic Impacts of Planned Major Events

Major events are a significant source of traffic congestion, especially in large metropolitan areas. This paper presents a case study of football games played at the Los Angeles Memorial Coliseum, a venue near downtown Los Angeles, California, with a capacity of about 80,000. Two teams play home games at the Coliseum: the Los Angeles Rams and the University of Southern California (USC) Trojans. These events take place in an area that has a high level of recurrent congestion. The traffic impacts of game days are analyzed by comparing game day traffic with traffic on control days on both the highway and arterial systems. The data include speed records from in-road detectors. Two sets of models are estimated to test relationships between game attributes and traffic performance. The first set is traditional regression models controlling for spatial and temporal correlation. The second set is random forest (RF), a type of machine learning estimation. RF is found to perform better, as it allows for complex non-linearities in variables. The results show that Rams and USC impacts are different. Rams fans arrive in a more concentrated time interval closer to the start time of games and, therefore, have a greater impact on the major approach routes than USC fans. The greatest impacts on highways are around nearby freeway-to-freeway interchanges. Arterial traffic is more consistently affected by distance from the venue. This case study provides the basis for better management of major planned events.