Advanced freeway tra c control strategies often rely on online optimization, which can be computationally intensive and limit their real-time applicability on large-scale networks. This study proposes a computationally e cient alternative: a linear parameter-varying gain-scheduled continuous-time linear quadratic regulator (GS–CT–LQR) to coordinate ramp metering (RM) and variable speed limiting (VSL) on mixed-tra c freeways. The approach uses a set of pre-computed feedback gains, derived from a multi-class cell transmission model, which are scheduled online based on real-time conditions like Cooperative Adaptive Cruise Control (CACC) market penetration, demand, and congestion state. A single quadratic Lyapunov function certifies uniform exponential stability and provides an input-to-state stability bound. The controller is comparatively evaluated on an 11 km corridor with measured demand in a comprehensive microsimulation study against baselines including no control, ALINEA, H1, and a model predictive controller. Across 25 to 75% penetration, the GS–CT–LQR improves throughput and average travel time, lowers CO2 emissions, and produces significantly smoother control actuation. It demonstrates superior robustness in stress tests involving demand surges, penetration drift, and measurement noise, all while achieving a median perupdate latency of just 0:13 ms on commodity hardware. The results confirm that the proposed approach o ers a practical and stable solution for real-time freeway tra c control, delivering the benefits of an adaptive strategy without the burden of online optimization.

Car-following (CF) behavior is the most abstract form of driving action and, CF behavior modeling has been one of the core aspects of traffic engineering studies for several decades. The literature about CF behavior modeling is vibrant and still evolving. Furthermore, the effect of CF models on the traffic flow performance through case studies on different traffic facilities is still being investigated. To shed light on this matter, this study presents a microsimulation-based case study considering a freeway stretch in Istanbul, Turkey, employing two different CF models, i.e., Intelligent Driver Model (IDM) and Wiedemann 99 through scenarios. Simulation of Urban Mobility (SUMO) is utilized as the microsimulation environment. Both CF models are calibrated according to the measurements. Scenarios for the comparative evaluation are setup based on the questions “What if German drivers used this freeway stretch? How much would the traffic flow performance change?" Using different case studies conducted in German Freeways on the literature, simulation model parameters are obtained for both models and, simulation analyses are performed. Traffic flow performances are evaluated based on the selected performance measures, such as throughput and total travel time. According to the findings, it is seen that results differ significantly between scenarios. We elaborate on the differences obtained and discuss the implications on different scenarios which are handled through different CF models.

The term “Mobility” is gaining new perspectives. Due to the paradigm shift driven by information technologies and autonomous vehicles, on-demand mobility services have experienced significant growth. Operating such a service efficiently is a challenging task. Especially, assigning vehicles to customers plays a vital role in this regard. To meet a satisfactory level of service while keeping the operational costs to a minimum requires efficient assignment strategies. Work summarized in this paper utilizes several shared and non-shared assignment algorithms in order to propose a methodology to assess the effects on the overall system performance for an Autonomous Mobility on Demand system. Selected algorithms are tested in a theoretical network with real-world taxi data with the help of microscopic traffic simulation software Simulation of Urban Mobility. Simulation scenarios are generated for both varying demand levels and increasing fleet sizes. Results suggest that for high demand levels and small fleet sizes, shared algorithms outperform non-shared algorithms for every performance measure chosen: total vehicle kilometers traveled, the ratio of empty fleet kilometers, average passenger waiting time for pick up, and the number of customers served in a period.

In this article, we propose a variant of the electric vehicle routing problem considering explicitly the intermediate nodes. Ultimately aiming to provide an optimal routing plan for the shuttle fleet that serves to a university settlement internally, we consider a real road network by explicitly taking into account in the formulation the entire intersections existing and the time-varying passenger demand at shuttle stops, as well as the vehicle dynamics, battery, and recharging features. On purpose, a mathematical program to obtain the joint minimization of a number of objectives in terms of cost, i.e., vehicle operating, battery recharging, and recharging station purchasing, is formulated. Solutions employing an exact method are sought using models of mixed integer program within scenarios involving a number of features including the campus-wide passenger demand, seat capacity of shuttles, battery capacity of shuttles, and recharging station location. Further solutions to our model formulation have been obtained using a benchmark set of instances designed for a large-scale real network. Our findings show that considering a real road network as it is, is significant in exact routing solutions despite the fact that the level of network complexity is an issue. We suggest that a trade-off among the actuality of the network topology and the consequent computational load should be carefully made in order to obtain solutions using exact methods. As is shown, there is room to investigate further in details the dynamics of routing considering especially the effects of changes in flow conditions at intermediate nodes using our formulation.

In the present study, we concentrate on the formulation and the exact solution of a specific vehicle routing problem with environmental concerns, which is a complex actual problem of staff service bus route planning for a university in a metropolitan city. Taking into account the air pollution related measures for representing the cost of environmental concerns in addition to the vehicle rental and fuel consumption related operational costs, we formulate a flow-based mixed integer linear program for the environment-friendly school bus routing problem specific to both homogeneous and heterogeneous vehicle fleets. We provide exact solutions to the instance sets we have designed specific to variants of the problem considering the fleet type, vehicle capacity, and maximum route length. Results from model based exact solutions for our formulations show up to 30% of reduction in the overall cost for the heterogeneous fleet case when compared to the homogeneous ones, and suggest a routing plan that enables the utilization of almost the entire capacity of all the buses assigned. We discuss in details the trade-offs between the cost items, travel times, and travel distances in composing a vehicle fleet considering the demand sprawled over the metropolitan area.

In this article we deal with the main critical issues of using electric vehicles in urban transport given the battery related limitations on driving range, and the network layout related limitations on the number of recharging stations. In this context, we seek the optimal routing plans together with the optimal locations of recharging stations for electric vehicle fleets through the Electric Vehicle Location Routing Problem with Intermediate Nodes (ELRP-IN) we propose. The ELRP-IN we formulate as a mathematical program considers the actual characteristics of battery discharging and recovering the braking energy. Energy consumption and recovery are determined through vehicle motion dynamics in conjunction with the 3-dimensional feature of the road geometry, passengers’/customers’ demands on getting on and off, and the pre-defined speed profiles, where the graph corresponding to the road network is extended with the explicit consideration of intersections. By alternating the objectives adopted in the ELRP-IN formulation, we discuss through a number of numerical experiments involving real case instances the effects of both the objective functions and the parameters, including the consumption and the gain of energy, cost, traveled distance, and travel time, on the routing plans. Highlighting the finding that energy is not recovered in all the sections with descending grade, we reveal the direct effect of the elevation on the energy consumption, and hence on the location of a recharging station, where we discuss as well the limitation on energy recovery.

In this study, we propose and test through micro-simulation a novel controller for the combined control of freeway traffic by adopting coordinated Ramp Metering (RM) and Variable Speed Limiting (VSL) strategies. In order to figure out the performance of the$H_\infty $State Feedback Controller we have designed, field observations on a real freeway segment with four on-ramps and an off-ramp in the city of Istanbul are used to calibrate the Intelligent Driver Model at SUMO (Simulation of Urban MObility). Scenarios with varying penetration rates of vehicles with Cooperative Adaptive Cruise Control (CACC) in mixed traffic are simulated through SUMO specific to the cases of no control, only coordinated RM control, and the combined coordinated RM + VSL control. Performance of the controller we have proposed has been analyzed considering a number of measures on traffic flow dynamics and emissions exhausted. We define three critical levels for penetration rates of vehicles with CACC in freeway traffic.

In this paper, a dynamic approach to specify flow pattern variations simulated by a multimode macroscopic flow model is followed, incorporating the neural network theory to reconstruct real-time traffic dynamics. In order to deal with the noise in and the wide scatter of traffic data, filtering is applied prior to overall modeling process. Filtered data are dynamically and simultaneously input to neural density estimation and traffic flow modeling processes. Traffic flow is simulated by modifying the cell transmission model in order to explicitly account for flow condition transitions considering wave propagations. Cell-specific flow dynamics are used to determine the mode of prevailing traffic conditions, which are, in turn, sought to be reconstructed by neural methods. The classification of flow patterns over the fundamental diagram is obtained by considering traffic density as a pattern indicator. The fundamental diagram of speed-density is updated to specify the current corresponding flow pattern. The modified classification returned promising results in capturing sudden changes on test stretch flow patterns that are simulated by the switching multimode discrete macroscopic model.

This paper investigates the performance of a flow model in providing efficient travel-time estimation for varying flow patterns of freeway traffic by adopting a two-phase fundamental diagram. The model follows a discrete-packet-based mesoscopic simulation approach that explicitly considers both the anisotropic property of traffic flow in packet state updating and the uniform speed differentiation of vehicle packets at each discrete time step. The measure of travel time is obtained as a link performance resulting from a simplified dynamic network loading process. The spatiotemporal flow propagation on a selected freeway segment is simulated comparatively by incorporating both the proposed model and a linear-travel-time-function-based link performance model. Performance of the flow model in travel-time estimation is sought, considering actual measures obtained by a probe vehicle. The main improvement on estimating the travel-time process is that the employed model considers different speed and acceleration levels on different discrete time intervals and satisfies the anisotropy property by consistently simulating flow propagation within the dynamic network modeling frame. In contrast to the vast data need and computational burden of trajectory-based methods, the employed flow-based model requires only the time-varying inflow profiles to estimate spatially and temporally varying travel times by artificially segmenting freeway routes.

In this paper, an analytical dynamic node-based model is proposed both to represent flows on a highway traffic network and to be utilized as an integral part of a dynamic network loading (DNL) process by solving a continuous DNL problem. The proposed model formulation has an integrate base structured with a mesoscopic link load-computing component that explicitly takes into account the acceleration behavior of discrete vehicle packets and an algorithm written with a set of nodal rules considering the constraints of link dynamics, flow conservation, flow propagation, and boundary conditions. The solution to the model formulation is obtained by simulation, where the coded algorithm of the proposed solution method is run after designing a discrete version of the problem. The performance of the proposed model, as a DNL model, is tested on a sample highway network following its validation study that is obtained on a sample highway node. It is seen that the proposed model provides consistent approximations to link flow dynamics. The new dynamic node model proposed in this paper is unique in that it encapsulates a mesoscopic approach in node-based flow dynamics modeling.

The need for a better representation of traffic dynamics and the reproduction of traffic flow motion on the network have been the main reasons to seek solutions for dynamic network loading (DNL) models. In this paper, a neural network (NN) approximator that supports the DNL model is utilized to model link flow dynamics on a sample network. The presented DNL model is constructed with a linear travel time function for link performances and an algorithm written with a set of rules considering the constraints of link dynamics, flow conservation, flow propagation, and boundary conditions. Each of the three selected NN methods, i.e., feedforward back-propagation NN, radial basis function NN, and generalized regression NN, is utilized in the integrated model structure in order to determine the most appropriate one, and hence, three DNL processes are simulated. Traffic dynamics such as inflow rates, outflow rates, and delays are selected to evaluate the performance of the proposed model.