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

Short-Term Travel Time Prediction on Freeways

Short-term travel time prediction supports the implementation of proactive traffic management and control strategies to alleviate if not prevent congestion and enable rational route choices and traffic mode selections to enhance travel mobility and safety. Over the last decade, Bluetooth technology has been increasingly used in collecting travel time data due to the technology’s advantages over conventional detection techniques in terms of direct travel time measurement, anonymous detection, and cost-effectiveness. However, similar to many other Automatic Vehicle Identification (AVI) technologies, Bluetooth technology has some limitations in measuring travel time information including 1) Bluetooth technology cannot associate travel time measurements with different traffic streams or facilities, therefore, the facility-specific travel time information is not directly available from Bluetooth measurements; 2) Bluetooth travel time measurements are influenced by measurement lag, because the travel time associated with vehicles that have not reached the downstream Bluetooth detector location cannot be taken at the instant of analysis. Freeway sections may include multiple distinct traffic stream (i.e., facilities) moving in the same direction of travel under a number of scenarios including: (1) a freeway section that contain both a High Occupancy Vehicle (HOV) or High Occupancy Toll (HOT) lane and several general purpose lanes (GPL); (2) a freeway section with a nearby parallel service roadway; (3) a freeway section in which there exist physically separated lanes (e.g. express versus collector lanes); or (4) a freeway section in which a fraction of the lanes are used by vehicles to access an off ramp. In this research, two different methods were proposed in estimating facility-specific travel times from Bluetooth measurements. Method 1 applies the Anderson-Darling test in matching the distribution of real-time Bluetooth travel time measurements with reference measurements. Method 2 first clusters the travel time measurements using the K-means algorithm, and then associates the clusters with facilities using traffic flow model. The performances of these two proposed methods have been evaluated against a Benchmark method using simulation data. A sensitivity analysis was also performed to understand the impacts of traffic conditions on the performance of different models. Based on the results, Method 2 is recommended when the physical barriers or law enforcement prevent drivers from freely switching between the underlying facilities; however, when the roadway functions as a self-correcting system allowing vehicles to freely switching between underlying facilities, the Benchmark method, which assumes one facility always operating faster than the other facility, is recommended for application. The Bluetooth travel time measurement lag leads to delayed detection of traffic condition variations and travel time changes, especially during congestion and transition periods or when consecutive Bluetooth detectors are placed far apart. In order to alleviate the travel time measurement lag, this research proposed to use non-lagged Bluetooth measurements (e.g., the number of repetitive detections for each vehicle and the time a vehicle spent in the detection zone) for inferring traffic stream states in the vicinity of the Bluetooth detectors. Two model structures including the analytical model and the statistical model have been proposed to estimate the traffic conditions based on non-lagged Bluetooth measurements. The results showed that the proposed RUSBoost classification tree achieved over 94% overall accuracy in predicting traffic conditions as congested or uncongested. When modeling traffic conditions as three traffic states (i.e., the free-flow state, the transition state, and the congested state) using the RUSBoost classification tree, the overall accuracy was 67.2%; however, the accuracy in predicting the congested traffic state was improved from 84.7% of the two state model to 87.7%. Because traffic state information enables the travel time prediction model to more timely detect the changes in traffic conditions, both the two-state model and the three-state model have been evaluated in developing travel time prediction models in this research. The Random Forest model was the main algorithm adopted in training travel time prediction models using both travel time measurements and inferred traffic states. Using historical Bluetooth data as inputs, the model results proved that the inclusion of traffic states information consistently lead to better travel time prediction results in terms of lower root mean square errors (improved by over 11%), lower 90th percentile absolute relative error ARE (improved by over 12%), and lower standard deviations of ARE (improved by over 15%) compared to other model structures without traffic states as inputs. In addition, the impact of traffic state inclusion on travel time prediction accuracy as a function of Bluetooth detector spacing was also examined using simulation data. The results showed that the segment length of 4~8 km is optimal in terms of the improvement from using traffic state information in travel time prediction models

Ramp metering and freeway bottleneck capacity

The objective of this study is to determine whether ramp meters increase the capacity of active freeway bottlenecks, and if they do, how. The traffic flow characteristics at twenty-seven active bottlenecks in the Twin Cities have been studied for seven weeks without ramp metering and seven weeks with ramp metering. A series of hypotheses regarding the relationships between ramp metering and the capacity of active bottlenecks are developed and tested against empirical traffic data. It is found that meters increase the bottleneck capacity by postponing and sometimes eliminating bottleneck activations (a 73 percent increase in the duration of the pre-queue transition period), accommodating higher (2 percent) flows during the pre-queue transition period, and increasing queue discharge flow rates after breakdown (3 percent). The two-capacity hypothesis about flow drops after breakdown was also examined and results strongly suggest the percentage flow drops at various bottlenecks follow a normal distribution (mean 5.5 percent, standard deviation 2.3 percent). The implications of these findings on the design of efficient ramp control strategies are discussed, as well as future research directions.transportation, travel behavior, congestion, ramp meters

Developing Sampling Strategies and Predicting Freeway Travel Time Using Bluetooth Data

Accurate, reliable, and timely travel time is critical to monitor transportation system performance and assist motorists with trip-making decisions. Travel time is estimated using the data from various sources like cellular technology, automatic vehicle identification (AVI) systems. Irrespective of sources, data have characteristics in terms of accuracy and reliability shaped by the sampling rate along with other factors. As a probe based AVI technology, Bluetooth data is not immune to the sampling issue that directly affects the accuracy and reliability of the information it provides. The sampling rate can be affected by the stochastic nature of traffic state varying by time of day. A single outlier may sharply affect the travel time. This study brings attention to several crucial issues - intervals with no sample, minimum sample size and stochastic property of travel time, that play pivotal role on the accuracy and reliability of information along with its time coverage. It also demonstrates noble approaches and thus, represents a guideline for researchers and practitioner to select an appropriate interval for sample accumulation flexibly by set up the threshold guided by the nature of individual researches’ problems and preferences. After selection of an appropriate interval for sample accumulation, the next step is to estimate travel time. Travel time can be estimated either based on arrival time or based on departure time of corresponding vehicle. Considering the estimation procedure, these two are defined as arrival time based travel time (ATT) and departure time based travel time (DTT) respectively. A simple data processing algorithm, which processed more than a hundred million records reliably and efficiently, was introduced to ensure accurate estimation of travel time. Since outlier filtering plays a pivotal role in estimation accuracy, a simplified technique has proposed to filter outliers after examining several well-established outlier-filtering algorithms. In general, time of arrival is utilized to estimate overall travel time; however, travel time based on departure time (DTT) is more accurate and thus, DTT should be treated as true travel time. Accurate prediction is an integral component of calculating DTT, as real-time DTT is not available. The performances of Kalman filter (KF) were compared to corresponding modeling techniques; both link and corridor based, and concluded that the KF method offers superior prediction accuracy in link-based model. This research also examined the effect of different noise assumptions and found that the steady noise computed from full-dataset leads to the most accurate prediction. Travel time prediction had a 4.53% mean absolute percentage of error due to the effective application of KF

Optimal Variable Speed Limit Control Strategy on Freeway Segments under Fog Conditions

Fog is a critical external factor that threatens traffic safety on freeways. Variable speed limit (VSL) control can effectively harmonize vehicle speed and improve safety. However, most existing weather-related VSL controllers are limited to adapt to the dynamic traffic environment. This study developed optimal VSL control strategy under fog conditions with fully consideration of factors that affect traffic safety risks. The crash risk under fog conditions was estimated using a crash risk prediction model based on Bayesian logistic regression. The traffic flow with VSL control was simulated by a modified cell transmission model (MCTM). The optimal factors of VSL control were obtained by solving an optimization problem that coordinated safety and mobility with the help of the genetic algorithm. An example of I-405 in California, USA was designed to simulate and evaluate the effects of the proposed VSL control strategy. The optimal VSL control factors under fog conditions were compared with sunny conditions, and different placements of VSL signs were evaluated. Results showed that the optimal VSL control strategy under fog conditions changed the speed limit more cautiously. The VSL control under fog conditions in this study effectively reduced crash risks without significantly increasing travel time, which is up to 37.15% reduction of risks and only 0.48% increase of total travel time. The proposed VSL control strategy is expected to be of great use in the development of VSL systems to enhance freeway safety under fog conditions

[[alternative]]A Study of Travel Time Prediction Models in Transportation Networks

計畫編號：NSC92-2211-E032-025研究期間：200308~200407研究經費：373,000[[sponsorship]]行政院國家科學委員

The effects of inclement weather conditions on freeway traffic operations

Adverse weather reduces the capacities and operating speeds on roadways resulting in congestion and additional productivity loss. Without a solid understanding of the mobility impacts of weather on traffic, freeway operators do not have the estimates of reductions in capacities and speeds to predict and simulate the impacts of traffic management strategies, when faced with inclement weather. Practically all traffic engineering guidance and methods used to estimate highway capacity assume clear weather. For major metropolitan areas in snow-belt states, inclement weather conditions occur during a significant portion of the year. This research classified weather variables by intensity and evaluated impacts of these weather categories on freeway capacity and operating speeds. The study area included Twin Cities metropolitan area freeways in Minnesota. The research database included 4 years of traffic data from roughly 4000 loop detectors and weather data over the same period from the five Road Weather Information System (RWIS) and Automated Surface Observing Systems (ASOS) sensors at three airports in close proximity to the freeway system. Results indicated that severe weather conditions caused the most significant reductions in capacities and operating speeds. Heavy rain (\u3e 0.25 inch/hour), heavy snow (\u3e 0.5 inch/hour), cold temperatures (\u3c-20° Celsius), and low visibility (\u3c0.25 mile) showed 10-17, 19-27, 6-10, and 10-12 percent reductions in capacities. Additionally, significant speed reductions of 4-7, 11-15, and 12 percent due to heavy rain, heavy snow and low visibility were obtained. Speed reductions due to heavy rain and snow were found significantly lower than those recommended in the Highway Capacity Manual 2000 (HCM 2000). Additionally, other weather variables presently not included in the HCM 2000, such as cold temperatures (\u3c-20° Celsius) and low visibility (\u3c0.25 mile) were classified by their intensities and investigated to identify their impacts on freeway capacities and operating speeds

A REAL-TIME TRAFFIC CONDITION ASSESSMENT AND PREDICTION FRAMEWORK USING VEHICLE-INFRASTRUCTURE INTEGRATION (VII) WITH COMPUTATIONAL INTELLIGENCE

This research developed a real-time traffic condition assessment and prediction framework using Vehicle-Infrastructure Integration (VII) with computational intelligence to improve the existing traffic surveillance system. Due to the prohibited expenses and complexity involved for the field experiment of such a system, this study adopted state-of-the-art simulation tools as an efficient alternative. This work developed an integrated traffic and communication simulation platform to facilitate the design and evaluation of a wide range of online traffic surveillance and management system in both traffic and communication domain. Using the integrated simulator, the author evaluated the performance of different combination of communication medium and architecture. This evaluation led to the development of a hybrid VII framework exemplified by hierarchical architecture, which is expected to eliminate single point failures, enhance scalability and easy integration of control functions for traffic condition assessment and prediction. In the proposed VII framework, the vehicle on-board equipments and roadside units (RSUs) work collaboratively, based on an intelligent paradigm known as \u27Support Vector Machine (SVM),\u27 to determine the occurrence and characteristics of an incident with the kinetics data generated by vehicles. In addition to incident detection, this research also integrated the computational intelligence paradigm called \u27Support Vector Regression (SVR)\u27 within the hybrid VII framework for improving the travel time prediction capabilities, and supporting on-line leaning functions to improve its performance over time. Two simulation models that fully implemented the functionalities of real-time traffic surveillance were developed on calibrated and validated simulation network for study sites in Greenville and Spartanburg, South Carolina. The simulation models\u27 encouraging performance on traffic condition assessment and prediction justifies further research on field experiment of such a system to address various research issues in the areas covered by this work, such as availability and accuracy of vehicle kinetic and maneuver data, reliability of wireless communication, maintenance of RSUs and wireless repeaters. The impact of this research will provide a reliable alternative to traditional traffic sensors to assess and predict the condition of the transportation system. The integrated simulation methodology and open source software will provide a tool for design and evaluation of any real-time traffic surveillance and management systems. Additionally, the developed VII simulation models will be made available for use by future researchers and designers of other similar VII systems. Future implementation of the research in the private and public sector will result in new VII related equipment in vehicles, greater control of traffic loading, faster incident detection, improved safety, mitigated congestion, and reduced emissions and fuel consumption

Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics

The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns. The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%