The interest in telecommuting stems from the potential benefits in alleviating traffic congestion, decreasing vehicle miles traveled (VMT), and improving air quality by reducing the necessity for travel between home and the workplace. Despite the potential economic, environmental, and social benefits, telecommuting has not been widely adopted, and there is little consensus on the actual impacts of telecommuting. One of the major hurdles is lack of a sound instrument to quantify the impacts of telecommuting on individuals’ travel behavior. As a result, the telecommuting phenomenon has not received proper attention in most transportation planning and investment decisions, if not completely ignored. This dissertation addresses the knowledge gap in telecommuting studies by examining several factors. First, it proposes a comprehensive outline to reveal and represent the complexity in telecommuting patterns. There are various types of telecommuting engagement, with different impacts on travel outcomes. It is necessary to identify and distinguish between those people for whom telecommuting involves a substitution of work travel and those for whom telecommuting is an ancillary activity. Secondly, it enhances the current modeling framework by supplementing the choice/frequency approach with daily telework dimensions, since the traditional approach fails to recognize the randomness of telecommuting engagement in a daily context. A multi-stage modeling structure is developed, which incorporates choice, frequency, engagement, and commute, as the fundamental dimensions of telecommuting activity. One pioneering perspective of this methodology is that it identifies non-regular telecommuters, who represent a significant share of daily telecommuters. Lastly, advanced statistical modeling techniques are employed to measure the actual impacts of each telecommuting arrangement on travelers’ daily activity-travel behavior, focusing on time-use analysis and work trip departure times. This research provides a systematic and sound instrument that advances the understanding of the benefits and potentials of telecommuting and impacts on travel outcomes. It is expected to facilitate policy and decision makers with higher accuracy and contribute to the better design and analysis of transportation investment decisions.

Bicycling as a mode of transportation has been increasing in recent years due to its environmental and health benefits. The availability of bicycles through bicycle share programs has made bicycling a more viable option. With this increase, there is a need for complementary improvements of bicycle infrastructure. Many local and regional transportation agencies are recognizing this need and developing a master plan or safety action plan to improve the city’s bicycle and walking facilities. This study examines bicycle travel demands and travel patterns in Lexington, Kentucky as generated by SPIN bicycle share users. It is hypothesized that the SPIN users emulate bicycle users on and around the University of Kentucky campus. Therefore, analyzing their travel patterns will provide a valuable understanding of bicycle demand and infrastructure needs. To identify such demand, travel patterns and routes were compared to the existing bicycle infrastructure in order to determine improvement needs with an ulterior goal to increase bicycling as a mode of transportation. The methods of study include five levels of analysis: length and duration, temporal, climatic, point density, and modeling. Recommendations for improving routes and parking facilities have been developed based on analytical methods and results obtained. The findings support the notion that bicycle infrastructure influences the travel paths cyclists take. The research supports the idea that commuters are using SPIN bicycles to chain their trips with transit and completing the last or first section of the trip with a bicycle. It was found that bicycle travel demand fluctuates with weather patterns. Furthermore, future work could use the existing data and conduct a detailed analysis on the individual trip level to determine what percentage of a completed trip was taken on an existing bicycle facility or on a non-facility. These findings should aid transportation planning and city officials to make decisions for expanding the existing bicycle network in efforts to minimize the percentage of cyclists who take a detour and the length of detours when necessary.

According to the US Department of Transportation, around 20 people die on a daily basis in a signalized intersection, with most of these resulting from angle or head-on collisions. The US-DoT’s Federal Highway Administration (FHWA) has identified modern roundabout intersections to be substantially safer than signalized intersections, due in part to the reduction in conflict points from 32 in a traditional signalized intersection to 8 in a modern roundabout. Despite the increased adoption of modern roundabouts across the US, there are a number of specific design elements for which the direct impact they have on operational and safety related performance of the roundabout remains unknown. To be specific, there is currently no conclusive research on the direct effects related to the introduction of a reverse curve (S-curve) on the approach to a roundabout. Moreover, what are the impacts of S-curves of varying geometries on the approach to a roundabout? This research employed a series of microsimulation-based analyses to investigate the speed related impacts related to the introduction to S-curves on the entry to a roundabout. An existing roundabout, in Amherst, MA, USA was used as a case study for this experiment. The data at each approach of the roundabout was collected by a static camera strategically placed to attain both the pedestrian and vehicle count during peak traffic hours. The data was manually reviewed to determine the upstream and downstream vehicle counts. The dimensions and angles of the existing roundabout were measured from Google earth and the image was extracted to AutoCaD Civil 3D. Since the objective is to check whether S-curves near an approach have a significant impact in speed, the deflection angle of the roundabout was not altered. The turning radius and angle at the approach was cross verified by measuring it on site. The existing roundabout was considered as the base model. The four approaches of the roundabout have different entry angles and radii. The revised models were drafted by strategically placing the S-curve at each approach and by steadily increasing their deflection angle and approach radius. The base and revised models cases were initially modelled, after which the conventional linear approach was modified to an S-curve and evaluated. Field data from the locations were to and calibrate microsimulation models on AIMSUN. The resulting trajectory data was analyzed for both the base case as well as three levels of experimental S-curves (ranging from 30 to 60 degrees) on each roundabout approach (16 total). The results provide evidence to suggest that a significant reduction in speed can be realized with a minimal amount of the reverse curvature on the roundabout approach. The trajectory output files were then imported into the Surrogate Safety Assessment Model (SSAM) to determine the number and type of conflicts experienced at each approach under each scenario evaluated in AIMSUN.

The transportation sector is considered one of the major contributors to greenhouse gas (GHG) emissions in metropolitan areas, and any efforts to reduce these emissions requires strategic management of multiple transportation modes. This paper presents a method to identify opportunities to reduce GHG emissions by expanding commuter bus services and incentives to shift commuters from private cars to transit. The approach uses a nested multinomial logit model for mode choice in a region that includes driving alone, carpooling, walking, cycling, and using four possible transit modes (ferry, commuter rail, rapid transit and bus) by walk access or driving access. A model of existing conditions was calibrated with data from the Boston metropolitan area. Using an emission factor model based on average speeds from the California Air Resources Board (CARB), the net effect of new commuter bus service on GHG emissions from transportation was estimated. Potential GHG reductions are weighed against the capital and operating costs of new transit services to quantify the cost-effectiveness of a new commuter bus service for isolated origin-destination pairs. This modeling framework is used to optimize fares and bus frequency in order to identify the corridors with the most cost-effective potential for GHG reduction. Results are presented for the Boston region, demonstrating the feasibility of implementation and the potential magnitude of benefits for cost-effectively reducing GHG emissions associated with transportation. The method is general and can be applied in other cities around the world.

Tripod, an integrated bi-level transportation management system, is a smartphone application from the potential user’s point of view which would be instantly accessed prior to performing the trip. Since there are constantly several alternatives for any trip, Tripod considers a series and combination of various parameters, including departure time, mode and route, and rewards for each alternative with a number of redeemable points for goods and services called “Tokens”. The framework responsible for computing the optimized number of tokens awarded to the set of available alternatives in order to minimize the system-wide energy consumption under a constrained Token budget, is the System Optimization (SO) implemented in Tripod. To do so, a higher number of tokens would be awarded to the alternatives, guaranteeing a larger energy saving, less energy consumption, alternatively. SO is multimodal whereby public transit, private car, carpooling, etc. are being considered as the potential travel modes. Furthermore, SO is dynamic, predictive and personalized: the same alternative is rewarded differently, depending on the current and predicted future condition of the network and on the individual’s profile. In order to solve this problem, a multimodal simulation-based optimization model will be elaborated.

School districts providing busing services for students who live too far to walk to school. In many districts a fleet of school buses is used in sequence to transport high school students, then middle school students, and then elementary school students. The result is that high school classes must start much earlier in the morning than the elementary school, and buses may traverse similar routes three times each morning and afternoon. In light of recent research on the benefits of later high school start times and the need to control transportation costs, school districts are seeking efficient school bus routing plans that meet student needs at low cost. This study uses 2018 data for schools in Northampton, Massachusetts, to identify the potential to achieve two objectives: 1) start the high school classes as late as possible in the day, and 2) minimize the cost of busing. The proposed procedure makes use of existing school bus data to optimize bus routes, which can be applicable for smaller cities. A revised routing plan that mixes high school and middle school students on the same buses allows the high school to start 30 minutes later while reducing total school bus operations.

While there are many proposals for fixing congestion between San Francisco and Oakland in California by adding a new bridge or tube, these solutions will take decades to implement even though a solution is needed now. This thesis assesses sixteen different strategies for reducing congestion in the short-term in the four categories of improving transit, promoting carpooling, implementing intelligent transportation systems practices, and incentivizing alternatives to using the Bay Bridge. Top priorities include HOV improvements on the West Grand Avenue and Powell Street onramps, altering WestCAT Lynx and BART transit services, partnering with rideshare apps to increase transit station accessibility (last mile problem), partnering with vanpool/minibus apps, promoting carpooling and implementing a citizen report system for carpool violators, shifting corporate cultures away from requiring employees to drive and drive alone, and lastly, altering land-use planning practices. To reach this conclusion, an inventory of current proposals and relevant research was compiled. Ridership and capacity data for the various modes of transportation across the bay were assessed for shortfalls and opportunities. Through this research and its resultant conclusions, focus can be placed on the best strategies to pursue in the near-term, while the Bay Area waits on a second bridge or tube in the long-term.

This thesis seeks to estimate CO2 emissions on a portion of the U.S. 101 highway in San Luis Obispo County before and after construction of a truck only lane on the Cuesta Grade. Towards that aim, the microsimulation software, VISSIM, was used in conjunction with the Environmental Protection Agency’s emissions model, MOVES. The microsimulation model was calibrated and validated against historical and present traffic volumes obtained from Caltrans with good results using several validation measures. It was found that CO2 emissions did decrease between 1998 and 2012 (pre and post lane addition), but this effect was shown to be different for the northbound (uphill) and southbound (downhill) directions. It was shown that the truck lane in the northbound (uphill) direction had a 9.5% decrease in volume with 10.7% decrease in emissions, and the southbound (downhill) direction had a 20.3% increase in volume but 7.4% decrease in emissions. For the northbound (uphill) direction, emissions seemed to correlate more closely with volumes, while the southbound (downhill) direction was less sensitive to these changes.

Adaptive ramp metering (ARM) is a critical component of smart freeway corridors under an active traffic management portfolio. While improving capacity through smart corridors and application of proactive traffic management solutions is less costly and easier to deploy than freeway widening, conversion to smart corridors still represents a sizable investment for a state department of transportation. Early evidence of improvements following these projects can be valuable to agencies. However, in the U.S. there have been limited evaluations, of smart corridors in general and ARM in particular, based on real operational data. This thesis explores travel time reliability measures for the eastbound (EB) Interstate 80 (I-80) corridor in the San Francisco Bay Area before and after implementation of ARM using INRIX data. These measures include buffer index, planning time, and measures from the literature that account for both skew and width of the travel time distribution. The measures are estimated for the entire corridor as well as corridor segments upstream of a bottleneck that historically have the worst measures of reliability. A new metric for measuring unreliability that may be derived from readily available INRIX data is also proposed in the thesis using data from the study corridor. While the ARM system is relatively new, the results indicate positive trends in measures of reliability even as the number of incidents on the corridor has increased in line with the national crash trends. The spatio-temporal trend evaluation framework used here may be used in the future to obtain more robust conclusions. However, since multiple smart corridor components were installed simultaneously, it may not be possible to fully isolate the effects of the ARM, or any of the other systems, individually.

This study reviews the competition and productivity of the U.S. freight transportation industry for the past 41 years. This study investigated the trends of HHI market concentration index values and labor productivity values of rail and truck sectors and tried to find any relationships between the two values in the separate periods before and after the abolishment of the ICC. This study also investigated how the existence of a regulatory body impacted productivity of the freight transportation industry by using a Cobb Douglas production function on annual financial statement data in the U.S. stock exchange market. This study found that: while the truck sector became more competitive after the abolishment of the ICC, the rail sector became less competitive, both sectors had a strong positive correlation between HHI and labor productivity, and the ICC’s abolishment resulted in positive changes of total factor productivity for the truck sector only.