Khani, Alireza

Transit services connect people to jobs and opportunities, fostering vibrant communities and multimodal travel along service corridors. A transit right-of-way (ROW) can help buses bypass congestion and stay on schedule. Many studies have proved that transit ROWs effectively improve service reliability and reduce user costs. However, these studies often focus on one or two service corridors, limiting comprehensive impact assessment. This project addresses this gap by investigating service reliability for all route segments across a transit system. We derived reliability metrics at the route segment level using high-resolution automatic vehicle location (AVL) and automatic passenger count (APC) data collected in the Twin Cities metropolitan area. We then collected and integrated data from various sources via spatial-temporal computing to capture service characteristics, operating environments, traffic conditions, and land-use features along route segments. We applied the Gradient Boosting Model (GBM) to examine nonlinear relationships between these factors and bus travel time reliability. Lastly, we used the trained model to estimate potential improvements in reliability with dedicated ROWs. Through these steps, we worked with members of the Technical Advisory Panel (TAP) to illustrate our methodology and demonstrate its utility for transit agencies. Specifically, the results proved that the ratio of bus lanes and busways was associated with more reliable travel time along route segments. We also found that route segments along a few service corridors with unreliable services can greatly benefit from implementing a dedicated ROW.

Public transportation provides a safe, convenient, affordable, and environmentally friendly mobility service. However, due to its fixed routes and limited network coverage, it is sometimes difficult or impossible for passengers to walk from a transit stop to their destination. This inaccessibility problem is also known as the “transit last-mile connectivity problem.” Such a lack of connectivity forces travelers to drive, thereby increasing vehicle miles traveled (VMT) on roads. The autonomous mobility-on-demand (AMoD) service, with characteristics such as quick fleet repositioning and demand responsiveness, as well as lower operational cost due to the elimination of operators’ wages, has the potential to provide last-mile coverage where fixed-route transit can only provide limited service. This study presents research on designing an AMoD service to solve the transit last-mile problem in Greater Minnesota. After selection of the Miller Hill Mall (MMH) area in Duluth, MN, as the case study site, analysis on local transit services and demand data show that passengers may have to spend significant time walking and cross multiple streets to access stores from transit stops. To address this issue, an AMoD system for last-mile service was designed and integrated with the fixed route transit service. Novel mathematical models and AMoD control algorithms were developed, and simulation experiments were conducted for evaluation of the AMoD service. Simulation results showed that the AMoD service can improve transit quality of service and attract more riders to use transit to the MHM area, and therefore reduce the VMT in the region. These findings were consistent with the literature in that mode choice and first-/last-mile access were highly interdependent and AMoD can improve transit quality of service and reduce VMT. Research on riders’ perception of AMoD service and field testing of the AMoD system using the developed models and algorithms are recommended to help agencies prepare for application of AMoD system in the region.

This project studies the benefits and barriers of increased adoption of medium-duty and heavy-duty electric trucks, referred to as e-trucks, and presents a methodology for optimizing the location of e-truck charging stations in Minnesota. In general, e-trucks provide zero tailpipe emissions and lower operating and maintenance costs. However, some barriers to adopting e-trucks include higher initial purchase costs, lack of charging and maintenance infrastructure, limited range, and charging time. The methods presented in this study aim to address the charging infrastructure planning, which provides information about e-truck charging activities, changes in vehicle miles traveled (VMT), and potential operating cost savings.

This project involved the development of innovative sharing-economy strategies to address rural transit challenges in Greater Minnesota. Many transit services and transportation network companies (TNC’s) like Uber and Lyft do not provide services to commuters outside metro areas, forcing most residents in Greater Minnesota to own automobiles. Meanwhile, many communities have school bus systems and substantial vehicle capacity that remain parked and unused much of the day. This project uses a human-centered design approach to engage a community in Greater Minnesota with a population of less than 10,000 people to develop a pilot for rural community transit that could be a model for similar communities across the state. The research seeks to answer the question of whether a shared, mobility services approach to rural transit transportation in Greater Minnesota could meet people's needs at a lower cost, with more convenience, and with greater positive impacts on the local economy than current transit practices and services. Our research developed a menu of strategies that uses existing community assets to promote walking, biking, car sharing, bus sharing, and car and van pooling.

The main objectives of phase 2 of this project were to obtain relevant data to calculate the percent remaining service life interval (PRSI) and two additional metrics and to perform Markov chain analysis and dynamic programming to determine how much time and funding is required to bring the system to a stable configuration, which allows for more consistent planning. First, relevant pavement management data was obtained from MnDOT and preliminary data analyses were performed. The prediction models and optimization process currently used by MnDOT were investigated and summarized. Next, two additional metrics, Asset Sustainability Ratio and Deferred Preservation Liability, were calculated for MnDOT’s network. Then details of the estimation process of state-to-state transition probabilities to be used in the Markov chain model were presented. To allow for site-specific variation, ordinal logistic regression models were incorporated in the Markov chain model. The results were used in a dynamic programming optimization methodology to obtain baseline and optimal policies for different scenarios and a user-friendly excel spreadsheet tool was developed. Finally, a summary of the work performed followed by conclusions and recommendations was presented.

Transitways such as light rail transit (LRT) and bus rapid transit (BRT) provide fast, reliable, and high-capacity transit service. Transitways have the potential to attract more riders and take a portion of the auto mode share, reducing the growth of auto traffic. Park-and-ride (PNR) facilities can complement transit service by providing a viable choice for residents who are without walking access to transit or those who prefer better transit service such as LRT or BRT. In this study, we conducted two research tasks on Transitways services in the Twin Cities region in Minnesota; 1) to examine the impact of the operation of the Green Line LRT on the annual average daily traffic (AADT) of its adjacent roads, and 2) to estimate a PNR location choice model in the Twin Cities metropolitan area.

In response to the limited awareness surrounding Bus Rapid Transit (BRT) and the A Line; this study provides answers to questions regarding the operation and public perception of the A Line in the Twin Cities region; Minnesota. Two traffic scenarios were studied; one for high-volume oversaturated traffic during the Minnesota State Fair; and a second for normal operating conditions. For both scenarios; intersection queue length and traffic flow rate were compared before and after an A Line bus. It was found that in both time periods (Fair and non- Fair); the dwelling of an A Line bus during a green traffic signal did not have a statistically significant impact on intersection queue length or traffic-flow rate at either of the two researched stations. From an analysis of the 2016 On-Board Survey; it was determined that passengers are more satisfied by the overall service of the A Line than local buses while there is not a significant difference in overall satisfaction compared to express buses; light rail and commuter rail. The top three important service attributes to overall satisfaction are "paying my fare is easy;" "hours of operation;" and "handling of concerns/complaints." It is recommended that the transit agency improve the attributes that have higher relative influences and lower mean performances. Based on this criterion; the attributes that should be given priority are "shelter/station conditions and cleanliness" and "behaviors of other passengers and atmosphere on board." This is report #16 in the Transitway Impacts Research Program series.