Diehl, Caleb. (2021). Examining the Effects of Common Carrier Lockers on Residential Delivery. http://hdl.handle.net/1773/47716. University of Washington Master's Thesis.
Activities of commercial vehicles just prior to or just following international border crossings are not well understood. Logistical responses to border crossings are believed to increase empty miles traveled, travel times and total vehicle emissions. Analysis of observational data and surveys taken by commercial carriers at the Cascade Gateway border crossings (between Whatcom County, Washington State and Lower British Columbia) improves understanding of the manner by and extent to which the border and the associated policies and regulations impact logistics operations near the border. Findings suggest that the border creates logistical incentives for trucks to both deadhead (cross the border without carrying goods as part of a cross-border round trip journey) and make staging stops near the border for border-related transloading. Policies such as cabotage laws and the Free and Secure Trade (FAST) program are both believed to increase the negative logistical incentives which the border creates. This thesis examines how these policies negatively impact logistical efficiency and suggests avenues to explore policy reform.
Electric vehicles, one of the emerging modes of transportation, are at the forefront of sustainable mobility. In the past years, there has been a rapid rise in EVs, both as private and public transportation modes. Private users are influenced by multiple factors while choosing electric cars as their travel modes. Among them, policy and infrastructure are deemed to be the main influencers globally. These policies and infrastructures vary in different cities. However, there is a lack of research dealing with what parts of the policy and infrastructure are actually most effective in EV adoption. This research presents a descriptive and quantitative evaluation as well as statistical analysis to identify the most effective policies and infrastructure components in electric car adoption as a personal transportation mode in sixteen selected cities; Seattle, Los Angeles, San Francisco, San Jose, New York, Oslo, Bergen, London, Amsterdam, Stockholm, Berlin, Munich, Paris, Shenzhen, Beijing and Tokyo. The cities are evaluated based on total electric vehicles on road, EVs on household level and electrification ratio of the registered cars in conjunction with household median income. Policy level incentives like electrification target, parking, toll, and lane access benefits along with tax rebates, subsidies and other monetary incentives as part of the total cost of ownership are also observed. Total number of public and residential charging points as well as the EV supply equipment program are analyzed as part of EV infrastructure preparedness on city level. Among the sample cities, Norway is the pioneer in the electric car integration into their passenger car market. All the sample cities have active Zero Energy Vehicle mandates and incentives for electric vehicles. Through secondary data collection via various online resources and statistical observation with help of the existing literature, this study found high correlation between EV ownership and incentives. Multilinear Regression Analysis model predicted 0.53% increase in passenger electrification with every $100 incentive increase. The environmental conditions of the sample cities are also evaluated to observe the impact of mass EV adoption in the overall improvement in CO2 emission reduction. At the end of this paper, this research proposes some policies to improve the EV adoption challenges present in the sample cities as well as the cities aiming to turn towards this sustainable mode in the future.
As commercial vehicle activity grows, the environmental impacts of these movements have increasing negative effects, particularly in urban areas. The transportation sector is the largest producer of CO2 emissions in the United States, by end-use sector, accounting for 32% of CO2 emissions from fossil fuel combustion in 2008. Medium and heavy-duty trucks account for close to 22% of CO2 emissions within the transportation sector, making systems using these vehicles key contributors to air quality problems. An important well-known type of such systems is the “pickup and delivery” in which a fleet of vehicles pickups and/or delivers goods from customers.
Companies operating fleet of vehicles reduce their cost by efficiently designing the routes their vehicles follow and the schedules at which customers will be visited. This principle especially applies to pickup and delivery systems. Customers are spread out in urban regions or are located in different states which makes it critical to efficiently design the routes and schedules vehicles will follow. So far, a less costly operation has been the main focus of these companies, particularly pickup and delivery systems, and less attention has been paid to understand how cost and emissions relate and how to directly reduce the environmental impacts of their transportation activities. This is the research opportunity that motivates the present study.
While emissions from transportation activities are mostly understood broadly, this research looks carefully at relationships between cost, emissions and service quality at an individual-fleet level. This approach enables evaluation of the impact of a variety of internal changes and external policies based on different time window schemes, exposure to congestion, or impact of CO2 taxation. It this makes it possible to obtain particular and valuable insights from the changes in the relationship between cost, emissions and service quality for different fleet characteristics.
In an effort to apply the above approach to real fleets, two different case studies are approached and presented in this thesis. Each of these cases has significant differences in their fleet composition, customers’ requirements and operational features that provide this research with the opportunity to explore different scenarios.
Three research questions guide this research. They are explained in more detailed below. The present study does not seek to provide a conclusive answer for each of the research questions but does shed light on general insights and relationships for each of the different features presented in the road network, fleet composition, and customer features.
In summary, this research provides a better understanding of the relationships between fleet operating costs, emissions reductions and impacts on customer service. The insights are useful for companies trying to develop effective emission-reduction strategies. Additionally, public agencies can use these results to develop emissions reductions policies.
Public transportation could be an important component of a solution to providing mobility while reducing traffic congestion and the environmental impact of transportation. However, from a customer perspective, a mobility choice is only a choice if it is fast, comfortable and reliable. This research looks at the reliability of public transportation and the use of easy-to-access information to combat the inherent unreliability and other barriers to increased use that exist in the system. The first section investigates the characteristics of transit service that are associated with on-time performance. The second and third sections discuss results of a survey and wait time assessment of OneBusAway, a real-time next bus countdown information source. The results of the survey indicate that OneBusAway users have an increased satisfaction with public transportation, as well as a perception of a decreased waiting time, increased number of transit trips per week, increased feelings of safety, and an increased distance walked compared with before they used OneBusAway. The follow-up study finds that for riders without real-time information, perceived wait time is greater than measured wait time. However, riders using real-time information do not perceive their wait time to be longer than their measured wait time. In addition, mobile real-time information reduces not only the perceived wait time, but also the actual wait time experienced by customers. The final three sections discuss other potential transit information tools that overcome the barriers to increased public transportation use. The Explore tool, an Attractions Search Tool, is described. Explore makes use of an underlying trip planner to search online databases of local restaurants, shopping, parks and other amenities based on transit availability from the user’s origin. In the fifth and sixth sections, the Value Sensitive Design process is used to brainstorm and assess additional transit tools from the user and the bus driver perspective. As a whole, this work gives credence to the notion that the power of improved access to information can help overcome the barriers to increased transit use.
Container terminals are important intermodal interfaces between marine and land transport networks. These interfaces have historically been sources of congestion and logistical inefficiencies. Exacerbated by growing trade volumes, the terminals have become bottlenecks in the port-related supply chain. This research explores using truck arrival information to integrate drayage truck and container terminal operations and improve intermodal system efficiency. The first part of the dissertation investigates whether and to what extent pre-arrival information regarding drayage trucks can be used to reduce operational inefficiencies and truck delays within the terminal. An advanced container rehandling strategy is proposed for using truck arrival information to reduce container rehandling work, and a computer simulation model is developed for evaluating the impact of truck arrival information on container handling efficiency by adopting the proposed strategy during the import container retrieval operation. In addition, a queuing model is employed to assess the impact of truck information on truck transaction time within a terminal. The research results demonstrate that any amount of information about arrival trucks is effective for improving yard crane productivity and reducing truck transaction time.
The second part of the dissertation investigates the travel time reliability of the port drayage network and evaluates the predictability of drayage truck travel time. A simple but effective method is developed for predicting the 95% confidence interval of travel time between any OD pair and is validated with GPS data. The research results indicate that the proposed travel time prediction method is quite accurate in estimating the arrival time window of trucks at the terminals. It is therefore sufficient to support the implementation of the proposed container rehandling strategy. Overall, this research provides terminal operators with insights as to the impact of truck arrival information on system efficiency of drayage truck/terminal operations, travel time prediction method to improve information quality, and operational strategies to effectively utilize such information. The research results can identify terminals likely to experience significant benefits if utilizing truck information, and inform the design of a data sharing system and tools for acquiring better information.
The growth of home deliveries, lower inventory levels and just-in-time deliveries drive the fragmentation of freight flows, increased frequency, more delivery addresses and smaller volumes. This leads to trucks inefficiently loaded and consequently more trucks in the road contributing to the growing congestion in cities. According to a study by INRIX and the Texas Transportation Institute, travelers in the U.S. are stuck 42 hours per rush hour commuter in their cars in 2014, that is twice what it was in 1982 and the problem is four times worse than in 1982 for cities of 500,000 people or less [28]. At the same time, a historical lack of integration of the freight transportation system into city planning efforts has left local governments unprepared. Under these circumstances, there is growing need for best practices for freight planning and management in U.S. cities. There is anecdotal evidence that the lack of areas for trucks to park and load/unload freight is one of the main causes of an inefficient urban freight parking infrastructure that leads to illegal parking and more congestion. The problem of lack of parking for freight loading/unloading has been studied with a focus on on-street parking. Meanwhile, the contribution of areas out of the public right of way (i.e. private) such as loading bays in buildings has not benefited from research. More importantly, the location and features of private freight parking are often unknown by local governments due to their private character.
This thesis presents the first predictive tool to estimate the presence of private freight loading/unloading infrastructure based on observable characteristics of property parcels and their buildings. The predictive model classifies parcels with and without these infrastructures using random forest, a supervised machine learning algorithm. The model was developed based on a rich geodatabase of private truck load/unload spaces in the City of Seattle and the King County tax parcel database. The performance of the random forest model was evaluated through cross-validated estimates of the test error. The distribution of the outcome variables is unbalance with over 90% of parcels without private freight infrastructure. To consider the problem of unbalance sample, the optimum model was set to maximize the area under the ROC curve (AUC). The authors investigated the confusion matrix and the model classifier was design to balance the sensitivity and specificity of the model. Model results showed AUC of 81.5%, a true positive rate of 82.1% and a misclassification error of 22.5%.
This research provides an assessment tool that reduces the field work required to develop a quality inventory of private freight loading/unloading infrastructure by targeting the parcel stock and making data collection methods more effective. Local governments can use this research to inform efforts to revise and update delivery operations and regulations of truck parking and loading.
The effective and efficient movement of freight is essential to the economic well-being of our country but freight transport also adversely impacts our society by contributing to a large number of crashes, including those resulting in injuries and fatalities. Technology has been used increasingly to facilitate safety and operational improvements within commercial vehicle operations, but motor carriers operate on small profit margins, limiting their ability to make large investments without also seeing an economic benefit from such technologies. This dissertation explores the economic implications associated with using onboard monitoring systems to enhance safety in commercial vehicle operations.
First, to better understand how electronic on-board systems work, paper-based methods of recording driver hours of service are compared to automated (or electronically recorded) hours of service for three motor carriers using process analysis. This analysis addressed the differences between manual (paper-based) and electronic methods of recording hours of service, specifically as they relate to the frequencies and magnitude of the errors. Potential errors are categorized by operations within an information-based process and the findings suggest that a reduction of errors can be achieved with an electronic system.
A benefit-cost analysis provides a better understanding of the economic implications of onboard monitoring systems from the perspective of the carrier. In addition to the benefits of reduced crashes, benefits associated with electronic recording of hours of service, reduced mileage, and reduced fuel costs are considered. A sensitivity analysis is used and demonstrates that on-board monitoring systems are economically viable under a wide range of conditions. Results indicate that, for some fleet types, reducing crashes and improving hours of service recording, provides a net benefit of close to $300,000 over the five-year expected lifespan of the system. Furthermore, when exploring additional benefits such as reduced fuel consumption and reduced vehicle miles, benefits can be upwards of seven times more than safety-related benefits. This research also shows that net positive benefits are possible in large and small-sized fleets. Results can be used to inform policies for motivating or mandating carriers to use such systems and to inform carriers regarding the value of system investment.
The demand for goods and services is rapidly increasing in cities, in part due to the rise in online shopping and more varied delivery options. Cities around the world are experiencing an influx of goods pickup and delivery activities. The movement of goods within urban areas can be very constraining with high levels of congestion and insufficient curb spaces. Pick-up and delivery activities, specifically those that are out of vehicle activities, encompass a significant portion of urban goods movement and inefficient operations can negatively impact the already highly congested areas and truck dwell times. This dissertation aims to provide insights and data-driven approaches to support freight plans in various cities around the globe with a focus on urban freight deliveries. To accomplish this goal, this dissertation first proposes to discover the current delivery process at the final 50 feet by creating value stream maps that summarize the flow of delivery activities and times, and time variations between activities. The map will be based on the data collected from five freight-attracting buildings in downtown Seattle. Secondly, this research explores contributing factors associated with dwell time for commercial vehicles by building regression models. Dwell time, in this study, is defined as the time that delivery workers spend performing out-of-vehicle activities while their vehicle is parked. Finally, this dissertation predicts the total time spent at the final 50 feet of delivery, including dwell times and parking-related times through discrete event simulations for various “what if” delivery scenarios. Multi-objective simulation-based optimization algorithms were further used to discover the optimal numbers of parking and building resources (e.g. number of on and off-street parking capacity, number of security guards or receptionists). This aims to better understand how increased deliveries in urban cities can impact the cost distributions between city planners, building managers, and delivery workers. This will also identify the areas for improvement in terms of infrastructure and resources to better prepare for the future delivery demands based on various scenarios.
The Port of Seattle surveyed drayage truckers serving the port in 2006, 2008, and surveyed drivers again in 2013 in partnership with the University of Washington. This thesis describes the methodology used to survey drayage drivers at the Port of Seattle, describes the economic conditions of drayage drivers at the port and changes in economic conditions since previous surveys, and attempts to model driver earnings based on other driver characteristics.
By increasing the number of days that the survey was distributed, and by soliciting driver feedback to make the survey understandable and relevant to drivers, the 2013 survey was able to gather a larger survey size than previous efforts (290 responses in 2013, compared to 99 responses in 2008 and 167 responses in 2006).
From 2008 to 2013, there was a reduction in the number of drivers working five or more days per week, from 80% in 2008 to 70% in 2013. The percentage of drivers doing work other than port trucking has increased from 8% in 2008 to 37% in 2013. Findings suggest that due to changing conditions at the Port of Seattle, there is a growing population of drivers that do port trucking as a part-time job in combination with other forms of work, rather than a full-time occupation.
Attempts at modeling driver earnings based on other factors (English as a second language, trip type, doing work other than port trucking, and average hours worked per week) did not discover strong relationships between these factors and earnings. It is recommended that future efforts in this area use higher resolution earnings data than the data available from the 2013 survey.