883 research outputs found
Complex Traffic Network Modeling & Area-wide Hierarchical Control
This thesis presents a novel methodology to divide a traffic region into subregions such that in each subregion a Macroscopic Fundamental Diagram (MFD) can be used to determine the state of that subregion. The region division is based on the theory of complex networks. We exploit the inherent network characteristics through PageRank centrality algorithm to identify the most significant nodes in the traffic network. We use these significant nodes as the seeds for a Voronoi diagram based partitioning mechanism of the network. A network wide hierarchical control framework is then presented which controls these sub regions individually and the network as a whole. At the subregion level a feedback controller is designed based on MFD concept. At the network level we develop a dynamic toll pricing algorithm to control the inflows into the network. This dynamic toll pricing is coupled with the subregion controller and thus forming a network wide hierarchical control. We use optimal control theory to design the dynamic toll pricing. A cost function is designed and then Hamilton-Jacobi-Bellman equation is used to derive an optimal control law that uses real-time information. The objective of the dynamic toll algorithm is to strike a balance between the toll price and optimal traffic conditions in each of the subregions. A case study is performed for the Manhattan area in New York city and results are provided through simulations
An Area-Aggregated Dynamic Traffic Simulation Model
Microscopic and macroscopic dynamic traffic models not fast enough to run in an optimization loop to coordinate traffic measures over areas of twice a trip length (50x50 km). Moreover, in strategic planning there are models with a spatial high level of detail, but lacking the features of traffic dynamics. This paper introduces the Network Transmission Model (NTM), a model based on areas, exploiting the Macroscopic or Network Fundamental Diagram (NFD). For the first time, a full operational model is proposed which can be implemented in a network divided into multiple subnetworks, and the physical properties of spillback of traffic jams for subnetwork to subnetwork is ensured. The proposed model calculates the traffic flow between to cell as the minimum of the demand in the origin cell and the supply in the destination cell. The demand first increasing and then decreasing as function of the accumulation in the cell; the supply is first constant and then decreasing as function of the accumulation. Moreover, demand over the boundaries of two cells is restricted by a capacity. This system ensures that traffic characteristics move forward in free flow, congestion moves backward and the NFD is conserved. Adding the capacity gives qualitatively reasonable effects of inhomogeneity. The model applied on a test case with multiple destinations, and re-routing and perimeter control are tested as control measures
The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions
Ramp metering, a traditional traffic control strategy for conventional
vehicles, has been widely deployed around the world since the 1960s. On the
other hand, the last decade has witnessed significant advances in connected and
automated vehicle (CAV) technology and its great potential for improving
safety, mobility and environmental sustainability. Therefore, a large amount of
research has been conducted on cooperative ramp merging for CAVs only. However,
it is expected that the phase of mixed traffic, namely the coexistence of both
human-driven vehicles and CAVs, would last for a long time. Since there is
little research on the system-wide ramp control with mixed traffic conditions,
the paper aims to close this gap by proposing an innovative system architecture
and reviewing the state-of-the-art studies on the key components of the
proposed system. These components include traffic state estimation, ramp
metering, driving behavior modeling, and coordination of CAVs. All reviewed
literature plot an extensive landscape for the proposed system-wide coordinated
ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE
- ITSC 201
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced
driver assistance systems and autonomous driving systems for safer and more
efficient trajectory planning. Conventional localization devices such as Global
Positioning System only provide road-level resolution for car navigation, which
is incompetent to assist in lane-level decision making. The state of art
technique for lane localization is to use Light Detection and Ranging sensors
to correct the global localization error and achieve centimeter-level accuracy,
but the real-time implementation and popularization for LiDAR is still limited
by its computational burden and current cost. As a cost-effective alternative,
vision-based lane change detection has been highly regarded for affordable
autonomous vehicles to support lane-level localization. A deep learning-based
computer vision system is developed to detect the lane change behavior using
the images captured by a front-view camera mounted on the vehicle and data from
the inertial measurement unit for highway driving. Testing results on
real-world driving data have shown that the proposed method is robust with
real-time working ability and could achieve around 87% lane change detection
accuracy. Compared to the average human reaction to visual stimuli, the
proposed computer vision system works 9 times faster, which makes it capable of
helping make life-saving decisions in time
Heterogeneous perimeter flow distributions and MFD-based traffic simulation
This paper investigates how network and traffic heterogeneities influence the accuracy of a simulation based on the Macroscopic Fundamental Diagram (MFD). To this end, the MFD modeling of a simple grid network is compared to the outputs of a mesoscopic kinematic wave model simulating traffic in the same network. Heterogeneous distributions of demand and supply at the boundaries are set to the local entries and exits of the mesoscopic model to generate heterogeneous network loadings. These boundary conditions challenge the MFD simulation, as significant discrepancies are observed between both modeling approaches in steady state. While the accurate calibration of the MFD and the average trip length can reduce the discrepancies for heterogeneous demand settings, no simple solution exists for heterogeneous supply settings, because they may drive very different internal congestion patterns in the network. We propose a correction method to adjust the MFD model outputs in such a case
Macroscopic fundamental diagram with volume-delay relationship: model derivation, empirical validation and invariance property
This paper presents a macroscopic fundamental diagram model with volume-delay
relationship (MFD-VD) for road traffic networks, by exploring two new data
sources: license plate cameras (LPCs) and road congestion indices (RCIs). We
derive a first-order, nonlinear and implicit ordinary differential equation
involving the network accumulation (the volume) and average congestion index
(the delay), and use empirical data from a 266 km urban network to fit an
accumulation-based MFD with . The issue of incomplete traffic volume
observed by the LPCs is addressed with a theoretical derivation of the
observability-invariant property: The ratio of traffic volume to the critical
value (corresponding to the peak of the MFD) is independent of the (unknown)
proportion of those detected vehicles. Conditions for such a property to hold
is discussed in theory and verified empirically. This offers a practical way to
estimate the ratio-to-critical-value, which is an important indicator of
network saturation and efficiency, by simply working with a finite set of LPCs.
The significance of our work is the introduction of two new data sources widely
available to study empirical MFDs, as well as the removal of the assumptions of
full observability, known detection rates, and spatially uniform sensors, which
are typically required in conventional approaches based on loop detector and
floating car data.Comment: 31 pages, 17 figure
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