A three-dimensional macroscopic fundamental diagram for mixed bi-modal urban networks

Recent research has studied the existence and the properties of a macroscopic fundamental diagram (MFD) for large urban networks. The MFD should not be universally expected as high scatter or hysteresis might appear for some type of networks, like heterogeneous networks or freeways. In this paper, we investigate if aggregated relationships can describe the performance of urban bi-modal networks with buses and cars sharing the same road infrastructure and identify how this performance is influenced by the interactions between modes and the effect of bus stops. Based on simulation data, we develop a three-dimensional vehicle MFD (3D-vMFD) relating the accumulation of cars and buses, and the total circulating vehicle flow in the network. This relation experiences low scatter and can be approximated by an exponential-family function. We also propose a parsimonious model to estimate a three-dimensional passenger MFD (3D-pMFD), which provides a different perspective of the flow characteristics in bi-modal networks, by considering that buses carry more passengers. We also show that a constant Bus-Car Unit (BCU) equivalent value cannot describe the influence of buses in the system as congestion develops. We then integrate a partitioning algorithm to cluster the network into a small number of regions with similar mode composition and level of congestion. Our results show that partitioning unveils important traffic properties of flow heterogeneity in the studied network. Interactions between buses and cars are different in the partitioned regions due to higher density of buses. Building on these results, various traffic management strategies in bi-modal multi-region urban networks can then be integrated, such as redistribution of urban space among different modes, perimeter signal control with preferential treatment of buses and bus priority

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

Empirical Estimation of a Macroscopic Fundamental Diagram (MFD) for the City of Cape Town Freeway Network

The City of Cape Town is the most congested city in South Africa, with Johannesburg coming in second. Capetonians are spending 75% more time in traffic because of the congestion during peak hours, thus reducing time spent on leisure and other activities. Due to population growth, increasing car ownership and declining capacity of rail infrastructure, Cape Town's road infrastructure will continue to be under severe pressure if the status quo is maintained. Research shows that congestion levels in urban areas are key factors in determining the effectiveness and productivity of the transport system. Traffic congestion poses a threat to the economy and the environment. Increasing corridors' capacity by increasing the number of lanes does not necessarily solve the problem. Effective urban traffic management and efficient utilization of existing infrastructure are critical in creating sustainable solutions to congestion problems. To achieve this, it is important that appropriate urban-scale models and monitoring strategies are put in place. Effective traffic management and monitoring strategies require accurate characterization of the traffic state of an urban-scale network. Several approaches, including kinetic wave theory and cell transmission models or macroscopic traffic simulation models, have been proposed and developed to describe the traffic state of an urban-scale network. However, these approaches are limited and require significant amounts of computational time and effort. The application of macroscopic fundamental diagram (herein referred to as MFD) to characterize the state of an urban-scale network has thus far proven to be more effective than other approaches. MFD represents the state of urban traffic by defining the traffic throughput of an area at given traffic densities. It describes the characteristics and dynamics of urban-scale traffic conditions, allowing for improved and sustainable urban scale traffic management and monitoring strategies. Against this backdrop, the existence of MFD for the City of Cape Town (CoCT) urbanscale network is yet to be established and the implications yet to be understood, as in other parts of the world. The main aim of this research was, therefore, to empirically estimate the macroscopic fundamental diagram for the CoCT's freeway network and analyse its observed features. To achieve this, observed data of 5 minutes periods for the month of May 2019 was used to estimate the MFD. The results confirmed that when the chaotic scatter-plots of flow and density from individual fixed loop detectors were aggregated the scatter nearly disappeared and points grouped neatly to form a clearly defined free-flow state, critical state and the formation of hysteresis loops past the critical density corresponding with the network observed maximum flow. Further analysis of the MFDs showed that a single hysteresis loop always forms past the critical density during the evening peak in a weekday MFD. However, it was inconclusive during the morning peak period in weekday MFDs. Lastly, an explicit hysteresis loop seldom appears in a Saturday MFD when the peak of traffic demand is lower than on weekdays. In order to understand the dynamics of the congestion spread, the freeway network was partitioned into penetrating highways network and the ring highway network. The results showed that the maximum flows observed for the two sub-networks were significantly different (943 veh/hr/lane for the penetrating highways network and 1539 veh/hr/lane for the ring highway network). The penetrating highways network's MFD indicated the presence of congestion in the network whereas the ring highway network indicated only the free-flow state (no indication of congestion) during peak periods. The congestion seen on the penetrating highways network was found not to be sufficiently spread on those highways. On the 24th May, congestion on the penetrating highway network was observed during both the morning and evening peak periods, whereas on the 31st May congestion was observed mainly during the evening peak period, with hysteresis-like shape. These observations confirmed that congestion during peak periods is not homogenously spread across the entire network, certain areas are more congested than others, hence the observed formation of hysteresis loops and slight scatters. Lastly, the hysteresis loops observed in the penetrating highways network's MFD was further characterized in terms of their shape and size. First, the results showed that the slight scatter and hysteresis patterns observed in penetrating highways network MFD's vary in size and shape across different days. The shapes of the hysteresis loops observed during both the morning and evening peak periods, were type H2 hysteresis loops, signifying a stable recovery of the network with the average network flow remaining unchanged as average network density decreases during the recovery. Characterization of the size of the observed hysteresis loops showed that the drop of the hysteresis (an indicator of network level of instability during recovery phase) was smaller, signifying a more stable network traffic and homogenous distribution of congestion during the recovery phase

Two-layer adaptive signal control framework for large-scale dynamically-congested networks: Combining efficient Max Pressure with Perimeter Control

Traffic-responsive signal control is a cost-effective and easy-to-implement network management strategy with high potential in improving performance in congested networks with dynamic characteristics. Max Pressure (MP) distributed controller gained significant popularity due to its theoretically proven ability of queue stabilization and throughput maximization under specific assumptions. However, its effectiveness under saturated conditions is questionable, while network-wide application is limited due to high instrumentation cost. Perimeter control (PC) based on the concept of the Macroscopic Fundamental Diagram (MFD) is a state-of-the-art aggregated strategy that regulates exchange flows between regions, in order to maintain maximum regional travel production and prevent over-saturation. Yet, homogeneity assumption is hardly realistic in congested states, thus compromising PC efficiency. In this paper, the effectiveness of network-wide, parallel application of PC and MP embedded in a two-layer control framework is assessed with mesoscopic simulation. Aiming at reducing implementation cost of MP without significant performance loss, we propose a method to identify critical nodes for partial MP deployment. A modified version of Store-and-forward paradigm incorporating finite queue and spill-back consideration is used to test different configurations of the proposed framework, for a real large-scale network, in moderately and highly congested scenarios. Results show that: (i) combined control of MP and PC outperforms separate MP and PC applications in both demand scenarios; (ii) MP control in reduced critical node sets leads to similar or even better performance compared to full-network implementation, thus allowing for significant cost reduction; iii) the proposed control schemes improve system performance even under demand fluctuations of up to 20% of mean.Comment: Submitted to Transportation Research Part C: Emerging Technologie

Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach

Prediction of taxi service demand and supply is essential for improving customer's experience and provider's profit. Recently, graph neural networks (GNNs) have been shown promising for this application. This approach models city regions as nodes in a transportation graph and their relations as edges. GNNs utilize local node features and the graph structure in the prediction. However, more efficient forecasting can still be achieved by following two main routes; enlarging the scale of the transportation graph, and simultaneously exploiting different types of nodes and edges in the graphs. However, both approaches are challenged by the scalability of GNNs. An immediate remedy to the scalability challenge is to decentralize the GNN operation. However, this creates excessive node-to-node communication. In this paper, we first characterize the excessive communication needs for the decentralized GNN approach. Then, we propose a semi-decentralized approach utilizing multiple cloudlets, moderately sized storage and computation devices, that can be integrated with the cellular base stations. This approach minimizes inter-cloudlet communication thereby alleviating the communication overhead of the decentralized approach while promoting scalability due to cloudlet-level decentralization. Also, we propose a heterogeneous GNN-LSTM algorithm for improved taxi-level demand and supply forecasting for handling dynamic taxi graphs where nodes are taxis. Extensive experiments over real data show the advantage of the semi-decentralized approach as tested over our heterogeneous GNN-LSTM algorithm. Also, the proposed semi-decentralized GNN approach is shown to reduce the overall inference time by about an order of magnitude compared to centralized and decentralized inference schemes.Comment: 13 pages, 10 figures, LaTeX; typos corrected, references added, mathematical analysis adde