International Conference on Transportation and Development 2020: Planning and Development

The focus of this study was on geographic and social equity in traffic safety. Literature review on equity, minorities, transportation equity, traffic safety equity, and emergency medical services (EMS) response time guided the development of a lengthy, electronic survey with 31 major questions. Five transportation equity questions and eight questions on rural safety were selected for in-depth analysis. People’s perception in EMS response between rural and urban areas is about the same. The respondents disagreed with (1) paying more taxes to improve EMS response in rural areas; (2) converting rural roads into high standard roads to make them safer; and (3) paying more taxes to raise the standard of rural roads. The 813 responses analyzed so far indicate that ambulance response in rural roadways, and hidden, missing, or defaced traffic signs were perceived as moderately problematic. Faded or worn out lane markings, and lighting at night, were perceived as problematic.

The purpose of this paper is to share lessons learned from administering the disadvantaged business enterprise (DBE) program on the Washington State Department of Transportation (WSDOT) State Route 99 tunnel design-build contract. This $1.09 billion contract contained an 8% DBE goal. When the design-build contractor announced that STP would not attain the goal, the DBE community contacted state and federal authorities to protest. An investigation by the Federal Highway Administration (FHWA) found that WSDOT was in noncompliance with its oversight duties of the DBE program. WSDOT entered into a conciliation agreement with FHWA to implement corrective actions and ensure that the contractor would achieve the goal or demonstrate a good-faith effort to do so. After working closely with FHWA and the contractor, the project was able to not only achieve the goal, but also to become a model for other projects and programs. As a result of these efforts, many lessons were learned, including that the owner needs to be proactive in its oversight of large megaprojects and the DBE goal needs to be carefully established to ensure attainability.

Resilience in infrastructure accounts for direct and indirect impacts associated with risks, criticalities, emergencies, and stakeholders. Infrastructure resilience is evaluated based on the infrastructure system’s ability to retrieve its functionality after a significant setback (e.g., natural disasters). Despite the complexities and threats posed to our infrastructure systems, there are limited scientific methods that evaluate the transportation resilience from the commuters’ perspectives. This study proposed a unique “8R Resilience Model” based on resilient concepts that aim to form an information management tool for assessing the disaster resilience of transportation infrastructure systems. The findings of the study present opportunities to influence policies and planning procedures to reassess the transportation systems by considering the commuters’ priorities, thus achieving resilient and sustainable transportation systems. Finally, the research provides an evaluation of the commuters’ assessments of transportation resilience that support infrastructure systems by implementing an analytical hierarchy process (AHP) and an ordered logit model on the collected data.

Existing research that focused on dynamic changes in the networks either studied the phenomenon on theoretical random networks or assumed the disruptions or failures in the real-life networks are completely random, which is hardly the case. Motivated by this observation, this paper has two interwoven objectives: On one hand, the vulnerability of road network under different types of targeted disruptions is characterized; on the other hand, a vulnerability measure based on the expected size of the giant components of the network under an uncertain disruptive event has been proposed. In order to achieve these objectives, first, the road network is represented as planar graphs and targeted disruption percolation patterns were simulated based on the values of the node -significance metrics. Second, the probability distribution of the giant component under uncertain disruptive events is modeled using the co-location index of the road network with flood control infrastructure. Third, percolations at different extents are simulated in order to estimate the giant components of the network under disruptions of different magnitudes, which, together with the probability estimated in the above step, is used to estimate the expected size of the giant connected component of the road network. The proposed method was applied to super neighborhoods in Houston during Hurricane Harvey. Comparisons are made among the vulnerabilities of different geographical locations, which lead to the identification of disaster-prone areas. It is found that the proposed method is capable of identifying areas that would have a lower level of connectivity as a result of a possible flooding event.

There is worldwide interest of transportation professionals to quantify traveler perceptions of system performance, ascertaining how such perceptions can differ from objective performance by traditional metrics. Such perceptions include the operational variability of vehicle travel times across hours and days of the week within highway transportation networks. Improvements to transportation infrastructure are informed by performance metrics; however, traditional methods evaluate delays based on deviations from a discrete or ideal condition. In this paper, we measure traveler perception with a novel approach of evaluating delays as deviations from the speed value with the maximum kernel density estimate (KDE). This approach provides a foundation for a risk-based multicriteria framework to inform stakeholders of appropriate reliability and safety mitigation methods. Recent advances in vehicular volume and speed data collection provide the disaggregate traffic data that depicts the variability across disparate time periods. The framework demonstrated in this paper informs enterprise operators and transportation agencies with new perspectives of relative congestion and infrastructure investment planning.

Intersection is a key node of urban traffic operation, and traffic efficiency of intersection is critical to an entire urban network. Telematics changes driver’s ability to perceive external information, which is different from driving behaviors in traditional traffic environment. This paper selects a new road intersection to simulate traffic operation under vehicle interconnection environment, and build a vehicle-vehicle experiment platform based on ZigBee short distance communication to collect data to analyze the start-up behavior of intersection and explore internal regulation of intersection operation in vehicle interconnection environment. The results show that start-up behavior at intersection is faster and start-up loss time is significantly reduced under vehicle interconnection environment. A method of saturation flow rate is used to calculate traffic capacity of signal intersection in vehicle interconnection environment, and the results show that capacity is increased by about 20%.

Traffic has a significant impact on public health. Traffic-related air pollution affects the health of individuals, especially when it comes to respiratory and cardiovascular health. A large number of people die due to respiratory and cardiovascular-related complications each year. According to the World Health Organization, 17.9 million people died because of cardiovascular diseases in 2016, representing 31% of all global death. Although the impact of the traffic-related air pollution on individuals is evident, the relationship between traffic-related air pollution and public health has been less investigated in the census tract level. The main goal of the study was to determine whether there was a relation between the health of a community, the traffic within them, and how they connect. Traffic and health data of the entire Fresno County were collected and then were analyzed at the census tract level. The results of this research showed a significant effect of traffic congestion on public health in the census tract level. The findings of this study could help in future planning and in the allocation of funds to help communities with health problems.

Transportation mobility is a critical element of the elderly’s quality of life. The rapid growth of the older population increases the importance of investigating the elderly’s travel behavior. The elderly’s mobility is often limited due to their physical abilities and financial constraints. By using data from the 2017 National Household Travel Survey (NHTS), the goal of this study was to explore the mobility patterns of the American elderly. The elderly’s number of trips is studied among different racial groups and different incomes. The number of trips has been compared for walk, bike, and transit trips. It was hypothesized that the number of trips of elderlies decreases by the presence of other person(s) at the house. In other words, if an elderly person lives in a house where the household size is more than one he/she has fewer trips than if he/she lives in a house where the household size is one. It was also hypothesized that the number of trips of the elderlies is affected by their race. Moreover, since income plays an important role in individuals’ travel behavior, the effect of income has been investigated for the elderlies. It was hypothesized that more amount of income, results in more number of trips. Hence, it is recommended to increase the public transportation quality where there is a huge elderly population who have low income. All analysis has been done using both NHTS 2017 and NHTS 2009 dataset, and the results have been compared. This study’s findings are valuable for the transportation planner by providing additional information about the elderly’s travel patterns.

Identifying travel modes from massive information is critical for travel demand modeling and transportation planning. Along with widespread application of the highly accurate position devices, the need of cost-effective approaches is more urgent than ever before. In the field of movement ecology, it has been proved by experiments that Hidden Markov Model (HMM) is much more efficient than traditional statistical classification method, such as Bayesian methods. Accordingly, in this paper, the Hidden Markov Model is adopted to predict travel modes based on raw GPS trajectories, where the modes are labeled as walk, bike, bus, and driving. Besides, this study also endeavors to examine the inherent statistical characteristics of trajectories in different modes, and identify the relationship between residents’ choice of travel mode and the traffic flow structure along the route. The data analysis results are believed to gain a deeper insight into the impact of distance and route on residents’ choice of travel mode, and it is expected to provide ideas and references for the transportation planning of cities with completed transportation infrastructure construction.

Travel time is an important performance measure in transportation network modeling. Providing accurate real-time information to travelers contributes to the reduction in day to day travel time variance and improves trip planning performance. The fuzzy set theory method is an essential contribution to transportation planning decision processes. The fuzzy optimization technique plays a vital role in transportation modeling with vague parameters. The present study aims to show the possibility of a non-linear fuzzy mathematical model to capture travel time information for transportation system users. Therefore, drivers can make informed decisions about their travel. The non-linear fuzzy mathematical model was introduced in multi-objective mathematical modeling to incorporate more than one objective in the design process. This helps engineers/analysts in the decision process. In this study, a fuzzy mathematical model is proposed to maximize the satisfaction levels considering minimal travel time and variability. The proposed mathematical model is applied to archived speed data from weigh-in-motion (WIM) sensors from multi-region in the State of Ohio in order for the model to capture the uncertainties for a wide range of variations such as vehicle drivers, weather, roadway, etc. The optimal solution has been identified using multi-objective fuzzy programming along with hyperbolic membership function. Fuzzy membership includes a range of satisfaction values between 1 (represents the maximum satisfaction level) and 0 (represents no association in the model). The results from the non-linear fuzzy model show better fitting of travel time variation compared to the linear membership function that has been used in past research. A linear membership function is not a suitable representation in many practical situations because it does not represent the real distribution of the data. The non-linear membership function describes the uncertainty and reflects the fuzziness of the actual situation of the data. The enhanced multi-objective fuzzy mathematical model may have an impact on the decision-maker travel behavior such as route choice and departure time. The experimental results exhibit the model’s capability to represent stochastic travel times and help decision-makers to solve problems with incomplete information under uncertain events.

The average travel time (expected travel time), 95th percentile travel time (planning time), planning time index (PTI), and buffer time index (BTI) are being widely explored and used by practitioners and researchers, as performance measures in the field of transportation engineering. However, literature documents limited efforts on travel time and reliability thresholds for use in transportation planning and operational analysis. The focus of this paper is to examine the inter-relationships between currently used macroscopic parameters such as density, volume-to-capacity ratio (V/C), and speed with the selected travel time based performance measures to establish level-of-service (LOS) thresholds, from planning perspective, for freeway links. The city of Charlotte, North Carolina, USA, was used as the study area to gather data, perform the analysis, examine the inter-relationships, and establish the thresholds. The findings indicate that density and V/C, computed from a calibrated regional network model, are non-linearly related to the average travel time, 95th percentile travel time, PTI, and BTI. The threshold values for different LOS letter-grades, for all travel time based measures, vary based on whether density or V/C is used to establish the relationships. Therefore, the applicability of fewer LOS or reliability threshold categories (for example, reliable, moderately reliable, and unreliable) based on statistical parameters were explored for possible implementation.

Transportation network design decisions can significantly affect the accessibility of a group or area, and therefore their economic development. Although the accessibility maximization and the equity of accessibility given investment budget have already been explored, the amount of transportation investment needed for an expected accessibility enhancement goal has never been investigated. This paper aims to propose a method and an algorithm to help policy makers to make the minimum investment decisions for the expected goal. A novel bi-level model system is formulated in which the upper level model is to minimize investment cost with a constraint of expected accessibility enhancement goal, while the lower level model is a transportation system equilibrium that is a feedback process between trip distribution and traffic assignment. A heuristic algorithm, namely trial and error algorithm, is designed to solve the proposed model. An experimental study is conducted to demonstrate the effectiveness and operability of the proposed method and algorithm. The results show that they are useful tools for investment decision making on goal-oriented accessibility enhancement.

Given the deepening awareness of the limited nature of fossil fuel reserves and the environmental consequences of consuming it, the increasing appetite for better public transportation infrastructure persists worldwide. A huge amount of investments and resources is being injected into the development of transportation infrastructures, including light rail, as the need for better and faster transportation is correlated with the economic development of a country. However, there is a serious lack of understanding regarding the underlying uncertainties, as well as their impact on the performance of these infrastructure projects. As a consequence, performance results that are lower than the forecasts are very common in rail transportation and financial or social benefit of light rail transits deviate hugely from what is expected. In many of these cases, either the overall cost of the project turns out to be much higher or the patronage proves to be far less than originally forecasted. This study aims at reducing such a gap in the literature by considering the uncertain nature of the demand. More specifically, this study proposes a real options-based framework for the feasibility analysis of the potential locations of light rail stations. First, it estimates the demand for each proposed station location considering the endogenous uncertainties involved in the forecasting of demand. Second, the feasibility of different station development scenarios is analyzed using the net present value (NPV) method. Third, a real options analysis is done on various development scenarios by considering the possible values of incorporating flexibility in the development. Fourth, based on the level and nature of the uncertainty in the station demand for each location, the economic benefits of incorporating flexibility for the station-development are quantified. Using a case study approach, the proposed model is applied for a station location on Dubai Metro Purple Line. The results suggest that recognizing the major uncertainties at an early stage and incorporating flexibility into the system design accordingly pays off under certain circumstances.

Dynamic OD flow plays an important role in transportation planning and management. In this paper, a dynamic vehicle OD flow estimation model of urban road network was developed using multi-source heterogeneous traffic flow data. First, in order to improve the accuracy of OD demand allocation, the GPS data, road topology, and land use attributes were considered to construct the network-level traffic zones. Second, the ALPR data and GPS data were combined to increase the accuracy of observable vehicle OD flow. Third, a Kalman filter model with linear state constraint was proposed to estimate the unobservable vehicle OD flow. Specifically, the state transition equation was developed using random walks. The observation equation with dynamic mapping relationship between OD flow and link flow was developed based on dynamic traffic flow distribution theory using data collected from microwave detectors and ALPR sensors. The linear state constraint was formulated with observed traffic demand of network-level traffic zones using ALPR data. Finally, the performance of the model was evaluated and analyzed with the field data of Kunshan, China. The results showed that the proposed model estimated link flows accurately and performed better than standard Kalman filter model.

The paper introduces a method for acquiring trip behaviors within walking distance by means of multiple big data. First, an optimal set of big data is selected from possible sets of big data in the transport sector to estimate the trip behaviors. Second, the authors propose a method of estimating trip volume. Finally, the proposed method is applied to a case study that has been carried out at Tachikawa Station of the Japan Railway Central Line in Tokyo in order to validate the proposed method. A field test with the use of the Wi-Fi packet sensor was conducted at 11 locations including stores and traffic nodes on 1st of September 2018. Based on interviews with the managers of the shopping sites observed by the Wi-Fi packet sensors, it was confirmed that the estimated trips were nearly the same as the actual situations. The authors have demonstrated the possibility of using Wi-Fi packet sensor data and mobile phone location data to acquire trip behaviors within walking distance.

Tourism road is the transport facilities with the dual function of transportation and tourism. In recent years, the construction of tourist highway in the country has formed a new wave, and many places have carried out the planning and construction of tourist routes. Especially in Hainan Province, the Government proposed to build the first complete provincial tourism road system. Based on the analysis of the characteristics and function of tourism road, a set of planning method for tourism road are proposed. The method is based on the traditional road network planning and layout methods and combine the tourism resources evaluation and landscape value evaluation methods. Firstly, the trend and demand forecast of tourist traffic and tourist highway are analyzed, and the accessibility demand of tourist attractions is investigated. Then the tourism road network can be built based on the traffic demand. Secondly, by constructing the tourism value evaluation system and evaluating the tourism value of the road network, the tourism road planning scheme based on the landscape value is gained. Finally, by integrating the results of the previous two stages, the comprehensive planning and layout scheme can meet the travel demand and provide good travel value experience. Through the eco-environment evaluation of the comprehensive layout planning scheme, the final layout plan of tourist highway is obtained. The method has been applied in the planning of tourism road in Hainan Province, and has achieved good effects.

The study of urban residents’ time allocation behaviors of daily activity-travel helps to understand their decision-making mechanisms, and take corresponding measures to effectively guide travelers’ travel behavior. Based on the analysis of travel data of ATUS in the U.S., this paper extracted the influencing factors of travelers’ activity scheduling. The joint selection process of activity participation type and activity duration on the continuous time axis is explained through sensitivity analysis. The results show that: (1) According to the activity scheduling behavior of travelers, it can be found that each trip has its next trip associated with it based on activity chain. (2) The covariate parameter estimation results based on Cox model show that factors such as age, race, marital status, family income, family size, activity date, total number of daily activities, and duration time of activity N+1 have significant influence on the duration time of activity N. (3) The estimated model suggesting that there is interrelated and constraint relationship between activity N and activity N+1 of travelers. Moreover, the strength of that relations changes significantly with type of activities. The competing risk models used in this paper are a useful tool for describing such differences. These fundamental works are help for enriching and expanding the travel behavior analysis theory and method.

Quantitative risk assessment and risk management approaches have been employed by many public agencies in recent years to develop more robust project cost and schedule estimates and improve project delivery performance. A retrospective review of the results from risk assessments conducted at various stages of project development can provide insight into the stability of the risk-based estimates over time, along with the effectiveness of risk management implementation on the project. The Nevada Department of Transportation’s (NDOT) “Project NEON” has reconstructed substantial portions of I-15 and U.S.-95 in central Las Vegas, Nevada, with an estimated cost at completion of roughly $950 million. A series of quantitative risk assessments were performed on the project between 2008, early in conceptual design, and 2019 as the project was nearing completion. This paper describes the risk assessment and risk management approach utilized by NDOT on this project, summarizes the cost and schedule estimate progression over the project life, and identifies lessons learned.

Roundabouts are commonly used as an intersection traffic control device due to its safety and improved operational characteristics. Several methods have been developed to analyze the performance of roundabouts. Most of these methods are related to countries where the traffic flow is homogeneous. SIDRA is one such method. This research aims to propose an improvement of SIDRA roundabout delay estimation model to cater to the mixed traffic condition in most of the developing countries. An urban roundabout in Kandy, Sri Lanka with four approaches was considered in this study. Data collection was done by video recordings and inventory surveys. Collected data shows that traffic flow at the considered location contains 40%–55% of motorbikes and three-wheelers. Two improvement methods were proposed to incorporate the mixed traffic condition in circulating flow and the entry capacity. The delay at each entry to the roundabout was determined using these adjustment methods and the original SIDRA method. Then the three methods were compared. Statistical analysis was done and the results showed that there is no significant difference between two mixed traffic adjustment methods. However, the adjusted methods estimate higher delay values than the original SIDRA method. Additionally, the modified methods and original SIDRA method have a significant difference only for three entries. When the entry width and the number of entry lanes are higher the three methods do not show any significant difference.

Accessibility-oriented transportation planning refers to the practice of focusing on provision of accessibility rather than mobility in order to achieve sustainability of the systems while meeting the goals of efficient transportation. Although accessibility as a planning concept has over 50 years of history, currently mobility still dominates as the primary goal in transportation planning. We argue that methodologies and data for effective accessibility-oriented transportation planning are indeed available. In this paper, we describe a case study that makes use of existing accessibility analysis methodologies and the latest GIS transit data. We use these resources to estimate economic benefits resulting from improved accessibility of a bus rapid transit project. The estimated monetized benefits can help agencies highlight the benefits of transit-oriented projects in the project programming stage of transportation planning process.

Accessibility in a geo-spatial context refers to the ease of reaching a variety of opportunities from a given location. Accessibility theories (and resulting metrics) have traditionally focused on quantifying access to specific opportunities (such as jobs), or focused on specific modes (such as car, bike, etc.). Such approaches often fall short of providing a comprehensive picture of the true accessibility potential of a location as a combination of multiple modes to multiple types of destinations. Addressing this drawback, a novel metric labeled the ‘Mobility Energy Productivity (MEP) Metric’ was developed at the National Renewable Energy Laboratory to quantify the mobility potential of a location to connect people to goods, services, and employment using a variety of modes, while accounting for time, energy, and affordability. The MEP metric has been integrated with advance travel behavior models to compute the changes in mobility potential for various future scenarios, such as introduction of automated vehicles, and/or electric vehicles—but does so at the aggregate, or average-citizen level. The MEP in its initial iteration is not customized to the particular socio-economic contingents, or even to an individual whose modal availability or pattern of trip making may substantially differ from the average. Addressing this gap, this research effort extends the MEP framework from a static state to a more tailored and dynamic state, one in which an individual, or group can customize the metric for their unique characteristics, such as modes, activity patterns, and time-of-day preferences. The extended MEP metric framework can now be integrated to assess the customized mobility energy productivity of an individual.

In the present world, global warming is one of the major problems where air pollution plays a vital role. Different studies showed that the transportation sector is one of the significant role players for air pollution. To reduce air pollution through transportation modes, scientists were working for a long time. Electric vehicles (EV) are important innovations, which is considered one of the best options to fight against air pollution as well as it has less maintenance and operating cost, and low noise emission. However, EV growth is suppressed due to less number of public charging stations, the appropriate position of the charging station, and uncoordinated charging in the charging stations. Charging stations in suitable places and charging time is a significant concern to operate and increase the number of EVs. Different researches reviewed the issues of charging infrastructure like-different technics for charging station placement, optimization techniques, and topologies of fast charging stations but very few did analyze or reviewed different modeling approaches or charging stations optimal localization models. This study has reviewed EV charging station related literature until 2019 and tried to find out the recent trends in charging infrastructure planning of EVs. This study also briefly discusses various mathematical models and algorithms to plan an EV charging station from relevant literature.

Sustainable transport is integral to our societies, there are persistent challenges to its sustainability. This paper proposes strategies for cooperation among academia, industry, and transport sector through transport research dissemination by establishment of transportation professional networking group to provide a platform for sharing of experiences and exchange of ideas amongst transport professionals and researchers. This group has organized five international conferences on transportation globally which required the dissemination of innovative researches and approaches that brought about safe and sustainable mobility solutions. With proper coordination, transport experts are poised to help solve problems at this level of complexity and importance. The paper concludes by highlighting the availability of potentials and critical mass in academia, government, and the private sector to attain global stature and tackle workforce development challenges in transport sector which depends on how the research dissemination and publication ecosystem is nurtured. The study made a couple of recommendations that would enhance collaboration in order to ensure that fundamental advances are made in the areas of focus for workforce development and sustainability of transport infrastructure as technological advances and their impacts on infrastructure planning.

The explosive growth of transportation network companies (TNCs) in the last decade has posed dramatic disruptions on the taxi industry. This paper investigates how ridesourcing services impacted the taxi industry from both spatial and temporal perspectives in the city of Chicago. With the taxi data from 2014 to 2019 and TNCs data in 2019 from the Chicago Data Portal, this study summarizes the evolution and characteristics of taxi and ridesourcing trips over time, and investigates the time and mileage utilization rates of taxi trips. The result shows that the taxi utilization rate rises again from 2017 to 2019 after decreasing from 2014 to 2017. The peak utilization rates for taxi services are always witnessed during the perk period. Finally, a clustering method was implemented to capture the spatial tendency for taxi and ridesourcing services year by year. More interestingly, an increasingly “centralized” taxi service both temporally and spatially is discovered in the city of Chicago.

Bridge owners face aging physical assets and increasing traffic demand resulting from urbanization and emerging autonomous freight and vehicles. The available public funding however can hardly keep up with the needs. The State of Washington has identified needed repairs on 6,080 bridges at an estimated cost of $9.2B. An innovative approach is proposed to transform how bridge assets in Washington State could be managed and financed using efficient and digital (tokenized) financing models. The informational inefficiencies derived from bridge traffic, structural health monitoring, historical repairs, and upgrades, coupled to remote sensing data from drones and satellites can be valued and priced, either as cash flows or equity value. These, in turn, are integrated into efficient financial instruments, including tokenization, to lower transaction cost and risks, and to overcome the limitation of small project size. This paper describes the framework of digitization, bridge performance analytics, and data market in the context of innovative financing, risks and uniqueness of Washington State transportation development.

In the last two decades, there is an increased demand for the infrastructure development but because of having constraints like finance or lack of experience and expertise in building and operating the facility has led governments to adopt a Public-Private-Partnership model which is popularly known as “PPP” model. PPP model encourages private sector to involve in development of infrastructure and in provision of public services. According to the World Bank, India is one of the leading countries in terms of readiness for PPPs. Government of India is looking forward for huge investments in India by private companies mainly in infrastructure development. The road sector, accounting for nearly 80% of all PPP projects, dominates the PPP scenario in India. Among various PPP models, the commonly adopted models in India are design-build-operate-transfer (DBOT), Build-own-operate-transfer (BOOT) and build-operate-transfer (BOT). Typically, PPP road projects are long term in nature and have many risks associated with them. Many of the past PPP projects have either been delayed or abandoned due to insufficient financial risk management. This paper focuses towards the financial risks associated with the BOT road projects in India with the real-world road projects data. The net present value (NPV)-at-risk method is used for the evaluation of critical risks. NPV-at-risk financial risks model is developed which can be used as decision tool. The developed financial risk model also uses Monte Carlo simulation for the consideration of the probability distributions associated with the input risk parameters. The developed model can provide better decision for risk evaluation of and investment in privately financed infrastructure projects. It can also be used for the identification of the most influential source of uncertainty for the financial returns.

As the most transportation infrastructure projects, the implementation of a new underground freight transportation (UFT) system is an irreversible economic decision. While an economic decision to implement a UFT system is highly susceptible to construction cost contingencies, the existing UFT economic assessments do not consider such uncertainties in their investment valuations. This paper exploits a discrete real options (RO) approach to incorporate construction cost uncertainties as well as managerial decision-making flexibilities in the investment valuation of UFT systems. A dynamic programming approach is used to provide an optimal timing scheme for investment in UFT systems. An exemplary UFT investment case is used to demonstrate the implementation of the RO approach. The value of the option to invest in the UFT system is calculated at any time in the future, considering the volatility in the transportation construction market and including the value of the managerial decision-making flexibilities. This analysis also offers a construction cost threshold that makes the UFT investment economically viable. Although the net present value analysis indicates that it is beneficial to immediately invest in the studied UFT project, the RO analysis portrays how specific contingent outcomes in the transportation construction market could affect this decision. It shows waiting for predictable downturns in the construction costs could result in a more lucrative project. This paper provides transportation capital planners with a framework to objectively evaluate the investment in UFT systems under high construction cost uncertainties.

Road pricing is one of the methods for mitigating urban and/or intercity traffic congestion problems. This study proposes a lane-based road pricing model where the investigated expressway trunk is classified into two types of lanes: a premium lane and a regular lane(s). The premium lane serves road users with urgent needs and would be willing to pay a higher fee to avoid traffic congestion. The developed model applies the marginal cost pricing principle with an aim to maximize the total social welfare. The numerical analysis results indicate that the social welfare of the lane-based, first-best pricing scheme is higher than that of the no-toll scheme. In addition, the total social welfare is monotonically increasing as the lane-based tolling distance is increased. The findings of this study have implications for the government and/or expressway operators in preparing a desirable congestion management policy.

In order to better understand the energy consumption performance of parallel plug-in hybrid electric vehicle (PHEV) and to analyze the impact factors under the hybrid driving mode, the working modes are identified for more accurate energy consumption analysis with real-world travel data. The methodology for identifying the different working modes is proposed. An unobserved fixed effects model is used to analyze the effects of different factors on energy consumption rate (ECR), such as state of charge (SOC), speed, time, and surrounding environment. Based on the real-world longitudinal travel data of 425 PHEVs in Shanghai with more than 300,000 trips, the results show that the ECR of the sample PHEVs saves up to 19.3% compared to the conventional gasoline vehicle Toyota Corolla. Compared with the BEV Citroen C-Zero, the ECR is 2.96 times of it. It is concluded that the ECR derived from our research based on real-world travel data is more accurate and comparable in different dimensions from the methods used by most of researchers, thus is very meaningful for the public to be informed of the energy consumption performance of vehicles.

In an effort to help the transit industry prepare better job descriptions for staff positions, this study looks at various job descriptions posted by the human resources department of a transit agency. The goal of the study was to establish a baseline for developing the job descriptions for each staff position. The implemented method identified the major keywords and concepts within the job descriptions. By detecting the keywords and keyword frequency within the job descriptions, the split of responsibilities between each position is visualized. The study then performed a causality analysis to identify the patterns between ranking within the organization and the corresponding keywords. This nested analysis has been rarely utilized in the literature, and the hierarchical organization is the best-matched algorithm for applying these methods to create meaningful patterns. The methodology is applied to a case study, a transportation sub-department within a county government department in Texas housing nine administrative, managerial, or supervisory roles. The results of this analysis help transform the data into knowledge for decision-makers and transit agencies. Comparison of the analysis outcomes highlights complementary staff positions (to facilitate effective teaming and project management) and/or overlap in duties and responsibilities that may present an opportunity for increased efficiency.