Search

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 p...

Recent advances in artificial intelligence and wireless communication technologies have created great potential to reduce congestion in urban networks. In this research, we develop a stochastic analytical model for optimal control of communicant autonomous vehicles (CAVs) at smart intersections. We present the automated network fundamental diagram (ANFD) as a macro-level modeling tool for urban networks with smart intersections. In the proposed cooperative control strategy, we make use of the headway between the CAV platoons in each direction for consecutive passage of the platoons in the crossing direction through non-signalized intersections with no delay. For this to happen, the arrival and departure of platoons in crossing directions need to be synchronized. To improve system robustness (synchronization success probability), we allow a marginal gap between arrival and departure of the consecutive platoons in crossing directions to make up for operational error in the synchronization process. We then develop a stochastic traffic model for the smart intersections. Our results show that the effects of increasing the platoon size and the marginal gap length on the network capacity are not always positive. In fact, the capacity can be maximized by optimizing these cooperative control variables. We analytically solve the traffic optimization problem for the platoon size and marginal gap length and derive a closed-form solution for a normal distribution of the operational error. The performance of the network with smart intersections is presented by a stochastic ANFD, derived analytically and verified numerically using the results of a simulation model. The simulation results show that optimizing the control variables increases the capacity by 138% when the error standard deviation is 0.1 s.

The advent of autonomous driving technologies has created a crucial need for upgrading conventional traffic control and lane management strategies in large cities. In this research, we design an optimal lane management strategy for corridors with a heterogeneous demand of human-driven, autonomous, and communicant autonomous vehicles (HVs, AVs, and CAVs). In a monocentric city setting, we dynamically control the inflow of the network by optimizing the size of CAV platoons in the corridors based on the instantaneous condition of the integrated system. These corridors can potentially have three types of lanes for vehicles with different levels of automation technology. We model the multiple lane type corridors as sets of parallel bottlenecks with general distributions of multiclass demand. The dynamics of the congestion in the network is also modeled using the macroscopic network fundamental diagram (MNFD). To study the impacts of the rise in the penetration rate of AVs and CAVs on the performance of the system, we derive a closed-form representation of the model. We show that the increase of the delay in the network with the rise in the penetration rate of AVs and CAVs can have a stable, an unstable, or a hybrid pattern. To optimize the system, we minimize a weighted summation of the experienced delay in the corridors and the total travel time in the urban network by optimizing the number of lanes of each type and the dynamic size of the CAV platoons. The results of the San Francisco case study show that implementing an optimal lane management strategy can reduce the experienced delay in the corridors up to 78% with a rise in the AV/CAV penetration rate. By dynamically controlling the size of the CAV platoons in the automated highway of the Bay Bridge, we limit the increase of the travel time in the downtown network as low as 5%.

This paper presents a continuum approximation model for the operating cost of demand responsive transit (DRT) systems in large urban networks. Applications of the proposed model shed light on ways demand and characteristics of the DRT system affect major components of cost: fleet, vehicle hours, and vehicle miles traveled. Verifying the relationship with empirical data, results show an accurate approximation of the operating cost for the paratransit system in New Jersey. Furthermore, we develop a systematic approach for evaluating the efficiency of policy implementations for DRTs. Finally, the circumstances where coordinated taxis could be a cost reduction strategy are identified.

The majority of existing navigation systems only account for a single aspect of the route choice, like travel time or distance, in finding the optimal route for the trips in the network. In this research, we first identify a range of diverse factors that travelers take into account in their route choice decision in the network. A stated preference survey is conducted to show the heterogeneity in the preferences of users and its dependence to the purpose of the trips over the weekdays and weekends. Interestingly, results of the survey show that road safety is the most influential factor in the route choice decision of the average participants over weekends, exceeding even the travel time, and participants give more importance to the scenic quality of the routes for their weekend trips in comparison to their weekday trips. The results of the second part of the survey also indicate that in 27% of the cases participants choose routes other than the ones suggested by navigation systems, and 33% of the times that they take the suggested routes, they modify these routes according to their own preferences. The partial inability of existing navigation systems to suggest the routes that match the preferences of users can be attributed to ignoring (1) the diversity in influential factors and (2) the heterogeneity in preferences of the users by these systems. We propose a dynamic mixed logit route choice model to include the effects of information and learning to estimate parameters of a multivariable utility function for individual users based on their own historical route choice data over time. Finally, we present the concept of a smart navigation system that can gather the required information from real-time and online sources to suggest the routes that best match the users’ own preferences.

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.

The macroscopic fundamental diagram (MFD) is introduced in recent studies to present the relationship between the flow and the density of the network in large urban regions (neighborhoods). The MFD can be also rescaled to approximate network outflow as a function of the vehicular accumulation of the system in the morning commute problem. In this research, we develop a bathtub model (macro-scale traffic congestion model) by combining Vickrey's (1969) model of dynamic congestion with the MFD to formulate the user equilibrium over the peak as an ordinary differential equation (ODE). This problem can be solved numerically to estimate the exact solution of the morning commute problem. Alternatively, the morning commute problem can be solved analytically by approximating the solution of the ODE using a well-behaved function. Here, we present a quadratic and also a linear approximation of the equilibrium solution for a semi-quadratic MFD, considering that the declining part of the MFD is shown to be well estimated by a quadratic function. To optimize the system, we present pricing strategies for network users (dynamic tolling) and employers inside the region (dynamic taxing) that can minimize the generalized cost of the system by keeping the outflow maximized over the peak. Finally, we compare the exact and the approximate solutions of the problem, and also the proposed pricing strategies of the region in a numerical example.

Travelers consider the average duration and the reliability of travel time when choosing their route. However, the relative importance of average travel time and reliability not only depends on the purpose of the trip, but also varies from one person to another. Users seek to minimize their travel costs leading to an equilibrium condition in which they choose routes in such a way that they cannot reduce the general cost of their own trip. In this paper, we adopt the concept of the efficient frontier to represent the equilibrium route choice of the heterogeneous users in a network under travel time variability. Then, we use the primary properties of the efficient frontier to propose a mathematical formulation for the route choice problem for a discrete or continuous distribution of sensitivity of users to variations in route travel times. An analytical-based algorithm is designed to assign the heterogeneous demand to the network. Efficiency of the proposed algorithm in solving the route choice problem is also compared in a numerical example with a classic iterative method with a smoothing factor.

Mass transit is a key component of a sustainable transportation system in urban networks. In this research, we propose a continuum approximation model to optimize the line spacing, stop spacing, headway, and fare of the transit system by minimizing a linear combination of (1) users generalized cost, (2) agency operating cost, and (3) external cost of the emission in the urban region. The design of the transit system can be optimized by minimizing the total cost of the transportation system in three different network allocation scenarios: (i) mixed network (Bus), (ii) dedicated lanes (BRT), and (iii) parallel networks (Metro).

The morning commute problem, introduced in Vickrey [1969. “Congestion Theory and Transport Investment.” American Economic Review 56: 251–260], addresses the equilibrium trip schedule of the users for commuting through a single bottleneck with fixed capacity over the morning peak. In this paper, we adapt the concept of the efficient frontier from Portfolio Theory [Markowitz, H. 1952. “Portfolio Selection.” The Journal of Finance 7 (1): 77–91] to propose an analytical solution to the morning commute problem with a general distribution of the schedule preferences over time and a continuous joint distribution of schedule penalty preferences over the population of the commuters. On this basis, we analytically derive the equilibrium arrivals of the heterogeneous commuters to the bottleneck given the independent probability distributions of earliness and lateness penalty factors. We also propose a method to retrieve independent probability distributions of the schedule penalty factors in the equilibrium ...

The operating cost of a demand responsive transit (DRT) system strictly depends on the quality of service that it offers to its users. An operating agency seeks to minimize operating costs while maintaining the quality of service while users experience costs associated with scheduling, waiting, and traveling within the system. In this paper, an analytical model is employed to approximate the agency's operating cost for running a DRT system with dynamic demand and the total generalized cost that users experience as a result of the operating decisions. The approach makes use of Vickrey's (1969) congestion theory to model the dynamics of the DRT system in the equilibrium condition and approximate the generalized cost for users when the operating capacity is inadequate to serve the time-dependent demand over the peak period without excess delay. The efficiency of the DRT system can be improved by optimizing one of three parameters that define the agency's operating decision: (1) the operating capacity of the system, (2) the number of passengers that have requested a pick-up and are awaiting service, and (3) the distribution of requested times for service from the DRT system. A schedule management strategy and dynamic pricing strategies are presented that can be implemented to manage demand and reduce the total cost of the DRT system by keeping the number of waiting requests optimized over the peak period. In the end, proposed optimization strategies are compared using a numerical example.

Autonomous vehicles can profoundly change parking behavior in the future. Instead of searching for parking, autonomous vehicle owners get dropped off at their final destination and send their occupant-free cars to a parking spot. In this paper, we study the impact of parking in a bi-class morning commute problem with autonomous and regular vehicles. We consider a spatial distribution of parking spaces, which allows us to capture the parking location of travelers. In the equilibrium condition, autonomous vehicles leave home later (than regular vehicles), and park farther away from their destination. The regular vehicle travelers, however, leave home sooner to park closer to the destination with a smaller walking distance. The reverse occurs if regular travelers abdicate walking and take a faster mode from their parking space to the city center. 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. The proposed parking pricing strategies incentivize commuters to adjust their travel schedules by charging a parking price that increases with distance from the city center. Hence, those who park closer to their destination have to pay less. This is a counter-intuitive finding at first which arises from a trade-off with the earliness penalty of users who park close to the destination. We show that system optimality is also reached by redistributing the parking supply. Our numerical experiments capture the impact the autonomous vehicle penetration rate on the performance of the system and the proposed parking pricing and design strategies.

Pricing is seen as a viable alternative to manage the demand for transportation facilities. While supply increase might aggravate the problem, pricing is envisaged to relieve large cities from adverse traffic effects (congestion and pollution, among others). Nevertheless, pricing has its own drawbacks, often overlooked by the operators of the networks. It will cause changes in the travel behavior of the different groups and their demands (shoppers, retailers, and even basic businesses/ employees). This paper presents an extensive review of the subject, and an equilibrium model to estimate the long---run effects of a cordon pricing scheme. The problem of designing a price for a Central Business District (CBD) cordon is formulated in this study as a bi-level optimization problem. The lower level problem is a joint trip production-distribution-mode choice-assignment problem, with interactions among three groups of the users (agents). The upper level problem is a multi-objective decision-making problem, where CBD cordon price (as decision variable) forms the alternatives. It monitors four important objectives, namely maximization of consumers' surplus, minimization of air pollution and congestion measures, as well as minimization of the internal migration of the retail employment. The latter is a main cause of the CBD degradation. The demands for shoppers and retailers are elastic, but that of the basic employees is assumed inelastic. Two test problems are examined and solved to show the behavior of the model. The results show that by employing under-pricing, or over-pricing, the cordon would lead to an unfavorable situation in that it would decrease the consumers' surplus, and increase pollution and congestion levels. Moreover, at certain price levels the rate of migration would drastically increase, while it might be relatively insensitive to price in other ranges.

In this research, we develop an optimal credit-based pricing scheme for a single bottleneck with time-dependent demand and heterogeneous user preferences. For this purpose, we first propose an analytical model for dynamically pricing the bottleneck. Accounting for the independent distributions of the schedule penalty factors and the value of time of the commuters can significantly improve the accuracy of the pricing strategy. Moreover, implementing such a strategy using a revenue-neutral credit-based pricing scheme allows the value of the credits to be determined by the interaction between the users in the equilibrium condition of the market. Hence, there would be no need to charge all commuters over the peak period. Instead, the proposed strategy allocates the money raised from the “credit buyers” to subsidize the commutes of the “credit sellers” in order to incentivize the commuters to form a uniform distribution of arrival times over the peak period. Using tradable credits to design a revenue-neutral pricing strategy for the bottleneck can raise public support by improving the social welfare of the users. The proposed credit-based pricing strategy also have broad applications for optimizing the urban networks using the concept of the macroscopic fundamental diagram (see Amirgholy et al., 2017).