Modeling Choice Problems with Heterogeneous User Preferences in the Transportation Network

Users of transportation systems need to make a variety of different decisions for their trips in the network, while their objective is to keep the generalized costs of their own trips minimized. In the transportation network, there is a diversity of different factors that can influence the decisions of the users, while the relative importance of these factors varies among the heterogeneous users with different trip purposes. Nonetheless, the cumulative result of the individual decisions of the users seeking to minimize their costs according to their own preferences leads to the user equilibrium condition in which no one can reduce his/her cost by changing his/her decision. In this research, we adapt the concept of the efficient frontier from portfolio theory (Markowitz, 1952) in finance in order to model the bicriterion choice behavior of users with heterogeneous preferences in transportation networks. We show that the efficient frontier has a set of primary properties that remains general in different problems. Thus, the primary properties of the efficient frontier can be employed to analytically model and solve different bicriterion choice problems in transportation. For the first application, we use these properties to propose an analytical model for the morning commute problem when there is a heterogeneity associated with preferences of the users (Vickrey, 1969; Daganzo, 1985). A dynamic pricing strategy is also proposed to optimize the bottleneck by minimizing the total cost for users. In addition to the morning commute problem, Vickrey’s congestion theory is also shown to have applications in modeling and optimizing the operation of the demand responsive transit (DRT) system with time-dependent demand and state-dependent capacity as queueing systems. The efficiency of the DRT system can be improved by implementing a dynamic pricing strategy. The analytical solution of the morning commute problem can be also extended for modeling and pricing the DRT system when there is a heterogeneity associated with the preferences of the DRT service users. For another application of the efficient frontier in modeling choice problems in transportation, we propose a traffic assignment model to account for the heterogeneity in sensitivity of the users to travel time reliability in a network under travel time variability. However, the proposed model can have wide applications in modeling the equilibrium condition of different multicriterion choice problems in transportation

Cost-Efficient Bridge Scour Health Monitoring using Commercial Sensors

Bridge scouring has been a major international issue regarding bridge health and the overall longevity of a bridge. A common bridge health concern such as scouring accounts for close to 60% of bridge failures in the United States and is a leading cause to a bridge being in critical condition. Traditional methods to combat this failure is to measure the scour depth to assess a bridge health. Due to safety concerns of the traditional method, this study proposes to monitor a bridge’s health using a vibration-based technique. At present, vibration-based techniques have yet to be utilized reliably in the field. The sensor system chosen for this study is the accelerometers. Acceleration data collected from the sensors can be translated into frequency and amplitudes to monitor bridge health status. A laboratory experiment is conducted within this study with an oscillating platform to simulate expected vibrations that would be seen within the field. Once laboratory verifications were done, the sensor system will be deployed in the field for further observations. Collected data from this study is expected to show distinction between oscillation behavior of a scour critical bridge and non-scour critical bridge when compared to the theoretical natural vibration of a bridge. The laboratory and field collected data from this study will be discussed in the symposium

Which service is better on a linear travel corridor: Park & ride or on-demand public bus?

This paper develops an analytical model to support the decision-making for selection of a public transport service (PTS) provision between park & ride and on-demand public bus (ODPB). The objective of the model is to maximise the total social welfare, which includes consumer surplus and operator’s net profit. The model is solved by a heuristic solution procedure and tested on an idealized linear travel corridor. The case study considers the effects from population density, density distribution, size of residential area, P&R station location, distance from the residential area to centre business area (CBD), as well as the changes of residential area layout and population growth. Results show that P&R fits for low population density area while ODPB is more suitable for high population density area. Population distribution type has little influence on the services’ social welfare. ODPB is a preferable service for the city which does not have advanced metro network. Besides, the investment time for building ODPB service in the planning horizon is discussed with consideration of the development of residential area

Parking design and pricing for regular and autonomous vehicles: a morning commute problem

Autonomous vehicles can profoundly change parking behaviour in the future. Instead of searching for parking, the occupants alight at their final destination and send their occupant-free cars to a parking spot. This paper studies the impact of parking in a morning commute problem with autonomous and regular vehicles. We simplify the complex problem with distinct cost functions to a classic bi-class problem that can be analytically solved. To optimize the system, we develop temporal and spatial parking pricing strategies and a new parking supply design scheme, as practical alternatives for the conventional dynamic congestion pricing

Optimal traffic operation for maximum energy efficiency in signal-free urban networks: A macroscopic analytical approach

The integration of artificial intelligence and wireless communication technologies in communicant autonomous vehicles (CAVs) enables coordinating the movement of CAV platoons at signal-free intersections. The capacity of signal-free intersections can be significantly improved by adjusting traffic variables at a macroscopic scale; however, the resulting improvement in the capacity does not necessarily have a positive impact on the energy consumption of CAVs at the network level. In this research, we develop an analytical model to enhance energy efficiency by optimizing macroscopic traffic variables in signal-free networks. To this end, we adopt a macroscopic modeling approach to estimate the operational capacity by accounting for the stochasticity resulting from the error in synchronizing the arrival and departure of consecutive platoons in crossing directions at intersections. We also develop a macrolevel analytical model to estimate expected energy loss during the acceleration/deceleration maneuver required for resynchronization at intersections as a function of synchronization success probability. We then maximize energy efficiency by minimizing expected energy loss and maximizing expected capacity in a biobjective optimization framework. We solve the energy efficiency problem using an analytical approach to derive a closed-form solution for the optimal traffic speed and the length of the marginal gap between the passage of consecutive platoons in crossing directions through intersections for a (general) normal distribution of the operational error. Having the closed-form solution of the energy efficiency problem, we balance the trade-off between energy loss and operational capacity at a large scale by extending the analytical model to the network level using the Macroscopic Fundamental Diagram (MFD) concept. The results of our two-ring simulation model indicate the accuracy of the proposed analytical model in estimating the macroscopic relationship between the expected energy loss at intersections and the vehicular density in signal-free networks. Our numerical results also show that optimizing the traffic speed and marginal gap length can improve energy efficiency by 31% at the cost of a 16% decrease in maximum capacity

Are Electric Vehicles Really Emission Free? Estimating the Increase in Air Pollutant Emissions from Power Plants in Georgia

The National Electric Vehicle Infrastructure (NEVI) Formula Program aims to expedite the development of public electric vehicle (EV) charging infrastructure in Georgia by investing $135 million over five years. By enhancing accessibility to charging stations for all users from various socioeconomic classes, the NEVI Formula Program facilitates the adoption of EVs, thereby significantly reducing vehicle air pollutant emissions. However, as the adoption of EVs and their total vehicle miles traveled (VMT) increase, the electrical energy consumption of EVs from the power grid, and consequently, emissions from power plants, are also expected to rise over time. This research employs the Cambium model, developed by the National Renewable Energy Laboratory (NREL), to show that the CO2, CH4, and N2O emission rates of power plants in Georgia during the evening and night hours, i.e., from 7:00 pm to 5:00 am, are respectively 1117%, 1010%, and 1569% higher than these rates during work hours, i.e., between 9:00 am and 4:00 pm. In contrast, the majority of EV charging also occurs in the evening and overnight when emission rates peak, i.e., from 7:00 pm to 5:00 am. In the next step, we are developing a machine-learning model to predict the air pollutant emissions of power plants in Georgia with the rise in the electrical energy consumption of EVs from the power grid

Traffic operation for longer battery life of connected automated vehicles in signal-free networks

Coordinating the movement of Connected Automated Vehicles (CAVs) can significantly improve traffic operations at signal-free intersections. However, we show there is a tradeoff between the operational capacity and the battery loss of CAVs at intersections. This research aims to enhance the battery life of CAVs with the minimum impact on operational capacity. To this end, we develop a stochastic model for the battery-capacity loss of CAV platoons at signal-free intersections. We account for the stochasticity in traffic operations at intersections by considering a probability distribution for the operational error in synchronizing the arrival and departure of consecutive platoons in crossing directions. We then balance the tradeoff between the battery-capacity loss rate and intersection capacity by optimizing the platoon size, traffic speed, and marginal gap length at a macroscopic scale. The numerical results of the research show that adjusting the macro-level control variables can improve CAVs\u27 battery life by 27.6% at the cost of a 3.5% reduction from the maximum capacity

Staggered work schedules for congestion mitigation: A morning commute problem

In urban networks, traffic congestion can be curbed by deconcentrating the temporal distribution of the travel demand. In this paper, we propose an optimal staggered work schedules problem to minimize the network total travel time and prevent the schedule delay in the trips of commuters over morning peaks in a bicentric network. The objective is to optimize the work start times of individual firms with minimum deviations from their initial schedules while taking into account that commuters choose their departure time selfishly to minimize their travel cost. We formulate the optimal work schedule problem in a bicentric network as a multi-objective optimization program that simultaneously minimizes the total travel time and the schedule deviation for the firms while satisfying near-equilibrium temporal conditions. The time-varying congestion dynamics are modeled using macroscopic fundamental diagrams. We solve the optimization problem for a test network and analyze the sensitivity of the Pareto solution to the policy parameters of the model. We assess the accuracy and effectiveness of the proposed method using an individual-level trip-based macroscopic simulation model. The numerical results demonstrate that implementing the proposed optimal staggered work schedules strategy accounting for commuters’ departure trip time choice can significantly reduce the traffic congestion in urban networks

Autonomous Vehicles on the Smart Roads: Challenges and Potentials

Self-driving vehicles and smart roads are not new concepts. These ideas have been discussed for many years but for much of this time, the required technology was not available to make them a reality. Just recently has our technology caught up to our ideas and we are beginning to see progress towards the realization of the automated highway system. As the transition to a complete automated system is still in progress, this leaves many questions that still need to be answered as well as allowing for novel solutions to be presented. Using modern research papers to formulate a sound basis in current automated highway systems research, novel solutions are presented for various problems still existing in this field

Balancing the efficiency and robustness of traffic operations in signal-free networks

Integration of artificial intelligence and wireless communication technologies in Connected Automated Vehicles (CAVs) enables coordinating the movement of the platoons of CAVs at signal-free intersections. The efficiency of the platoon coordination process can be improved by reducing the spacing between successive platoons to increase capacity; however, such improvement in efficiency can have adverse impacts on the robustness of the coordination process. In this research, we balance the trade-off between the efficiency and robustness of traffic operations in signal-free networks at a macroscopic scale. To this end, we use a rule-based approach to express the process of coordinating CAV platoons at intersections as a set of governing equations that provide an analytical basis to develop a stochastic model for traffic operations. We derive the platoon synchronization success probability for a general distribution of the error in synchronizing the movement of platoons in crossing directions and formulate the expected capacity as a function of the synchronization success probability. We then balance the trade-off between efficiency and robustness at a macroscopic scale by adjusting the average spacing set between successive platoons. In urban networks, adjusting the spacing between successive platoons also changes the vehicular density and consequently the traffic speed. We account for the interrelationship between the traffic speed and inter-platoon spacing in balancing the trade-off between the efficiency and robustness of traffic operations using the concept of the Macroscopic Fundamental Diagram (MFD) and extend the stochastic traffic model to the network level. We evaluate the analytical results of the research using a simulation model. The numerical results of the research show that optimizing the system by adjusting the platoon spacing can improve robustness by 13% at the cost of a 4% reduction from the maximum capacity at the network level