Managing Highways for Better Reliability

With the emergence of connected vehicle technologies, the potential positive impact of connected vehicle guidance on mobility has become a research hotspot by data exchange among vehicles, infrastructure, and mobile devices. This study is focused on micro-modeling and quantitatively evaluating the impact of connected vehicle guidance on network-wide travel time by introducing various affecting factors. To evaluate the benefits of connected vehicle guidance, a simulation architecture based on one engine is proposed representing the connected vehicle–enabled virtual world, and connected vehicle route guidance scenario is established through the development of communication agent and intelligent transportation systems agents using connected vehicle application programming interface considering the communication properties, such as path loss and transmission power. The impact of connected vehicle guidance on network-wide travel time is analyzed by comparing with non-connected vehicle guidance in response to different market penetration rate, following rate, and congestion level. The simulation results explore that average network-wide travel time in connected vehicle guidance shows a significant reduction versus that in non–connected vehicle guidance. Average network-wide travel time in connected vehicle guidance have an increase of 42.23% comparing to that in non-connected vehicle guidance, and average travel time variability (represented by the coefficient of variance) increases as the travel time increases. Other vital findings include that higher penetration rate and following rate generate bigger savings of average network-wide travel time. The savings of average network-wide travel time increase from 17% to 38% according to different congestion levels, and savings of average travel time in more serious congestion have a more obvious improvement for the same penetration rate or following rate.

Commuters’ valuation of travel time variability

The 6th edition of the Highway Capacity Manual (HCM-6) includes the concept of travel time reliability (TTR), which attempts to determine the distribution of average trip travel times over an extended period. TTR is an inherent part of travelers’ route choice decisions and is used by traffic managers to better quantify operations rather than simply using average travel times. The focus of this paper is on the HCM-6 urban street TTR methodology contained in Chapter 17. The approach uses historical data (e.g., weather and volume fluctuations) and simple empirical data (e.g., 1-day volume count) to provide the user with average travel time and reliability predictions for a traffic facility over an extended period (e.g., a year). To the best of the authors’ knowledge, there is no existing literature on validating the HCM-6 methodology with empirical data. The goals of this paper were to validate the HCM-6 urban street reliability methodology by comparing the empirical Bluetooth (BT) travel time distributions with the estimated HCM-6 distribution, and to propose potential HCM-6 augmentation strategies. Archived BT data from a 0.5-mi urban arterial in Lincoln, Nebraska was used for comparison. It was found that there were statistically significant differences, but minimal practical differences, between the mean of the predicted HCM-6 travel time distribution and the mean of the empirical distribution. However, the HCM-6 distribution had a lower variance than the empirical distribution. Not surprisingly, the HCM-6 model underestimated the TTR metrics (buffer index and the planning time index) by approximately 62% and 9%, respectively.

Ramp metering has been found to improve traffic conditions on the freeway mainline by breaking the platoons of ramp vehicles minimizing turbulence at the merge locations. The majority of the ramp metering evaluation studies have examined traffic performance under specific demand conditions, whereas travel time reliability and variability aspects have not been adequately addressed. This paper focuses on evaluating two well-known ramp metering algorithms in terms of travel time reliability as well as other performance measures such as queue lengths, throughput, and congestion duration, looking at a wide range of traffic demands throughout a calendar year. The evaluation was done through simulating an 8-mile corridor in Kansas City, KS. The results showed localized improvements due to ramp metering at the northern section of the facility, in terms of travel time reliability, throughput, and congestion duration. It was also shown that ramp metering may cause a new (possibly “hidden”) bottleneck to occur downstream, thus diluting its overall benefits when looking at an entire freeway facility. It is further noted that although ALINEA performed better than HERO on the mainline, traffic operations on the on-ramps significantly deteriorated using isolated control.

Evaluating the impact of public transport travel time inaccuracy and variability on socio-spatial inequalities in accessibility

Exploring travel time distribution and variability patterns is essential for reliable route choices and sophisticated traffic management and control. State-of-the-art studies tend to treat different types of roads equally, which fails to provide more detailed analysis of travel time characteristics for each specific road type. In this study, based on a vast amount of probe vehicle data, 200 links inside the Third Ring Road of Beijing, China, were investigated. Four types of roads were covered including urban expressways, auxiliary roads of urban expressways, major roads, and secondary roads. The day-of-week distributions of unit distance travel time were first analyzed. Kolmogorov-Smirnov test, Anderson-Darling test, and chi-squared test were employed to test the goodness-of-fit of different distributions and the results showed lognormal distribution was best-fitted for different time periods and road types compared with normal, gamma, and Weibull distribution. In addition, four reliability measures, that is, unit distance travel time, coefficient of variation, buffer time index, and punctuality rate, were used to explore the day-of-week travel time variability patterns. The results indicated that urban expressways, auxiliary roads of urban expressways, and major roads have regular and distinct morning and afternoon peaks on weekdays. It is noteworthy that in daytime the travel times on auxiliary roads of urban expressways and major roads share similar variability patterns and appear relatively stable and reliable, while urban expressways have most reliable travel times at night. The results of analysis help enable a better understanding of the volatile travel time characteristics of each road type in urban network.

Variation in Patients' Travel Times among Imaging Examination Types at a Large Academic Health System

: The anisotropic ocean environment will lead to variability of ocean acoustic travel time. The variability of travel times comes from several ocean dynamical processes, including eddies, internal tides, and stochastic internal waves. This study analyzes time series of travel time from the Philippine Sea 20102011 experiment conducted by the Scripps Institution of Oceanography. In this experiment, a pentagonal array of acoustic transceivers of radius 600-km transmitted 250 Hz pulses for the purpose of observing acoustic variability at multiple time and space scales. Using filtering methods, this study separates variability in travel time in bands associated with eddies, internal tides, and stochastic internal waves. The observed fluctuations in the internal wave band are compared to a simple theoretical model. The result of the research shows that over the year, eddies induce the largest amount of variability. Internal tides and internal waves show comparable fluctuations. There is some seasonal variability. Eddies and internal waves show fluctuations that are fairly isotropic across the array, while internal tides give highly anisotropic fluctuations. This anisotropy is related to the strong directionality of the internal tides that emanate from the Luzon Strait.

Basic reliability issues are addressed in this study, which is not concerned with average travel times, but rather ways of describing travel times that reflect the uncertainty in the amount of time required to travel between two points. Some of the uncertainty is systematic, such as the normal ebb and flow of traffic within the course of a work day or season of the year. Congestion associated with this systematic uncertainty is called recurrent. Congestion due to unpredictable or unexpected events is called nonrecurrent. If every travel time observed over a highway section for a year is plotted, a distribution of travel time is obtained. This plotted distribution is the picture of travel time variability. Such distributions are the focus of this research, especially the degree to which recurring and nonrecurring congestion influence the nature of the distribution. This research shows how to derive performance measures from such distributions and recommends a set for use by managers, planners, and systems operators. The research reexamines the composition of the congestion puzzle in terms of the fractions attributable to recurrent and various sources of nonrecurrent congestion. The project team used before-and-after studies to determine the effectiveness of different types of actions, both operational and capacity improvements, in improving reliability. This study also examined the effect of the downturn of the economy on travel time reliability. Finally, this research resulted in two types of models that can be used to estimate or predict travel time reliability. These models have broad applicability to planning, programming, and systems management and operations.

Transportation agencies are implementing traffic management strategies to improve mobility and safety on freeways. Ramp metering is a traffic management strategy deployed to mitigate congestion on freeways using traffic signals installed at entrance ramps to control and regulate vehicle entry onto the freeway mainline. Estimating the mobility benefits of ramp metering is critical to determine the strategy's effectiveness and inform the decision-making process regarding its deployment. However, the extent of the impact of ramp metering on recurrent congestion varies across studies. Among the reasons for the inconsistencies are the limitations of conventional methods for evaluating benefits, including the before-and-after approach, shutdown experiment, and traffic simulation. In addition to alleviating recurrent congestion, ramp metering has the potential of improving traffic conditions during non-recurrent congestion. Few agencies have used ramp metering to reduce non-recurrent congestion resulting from traffic incidents and adverse weather. Nonetheless, the ramp benefits during non-recurrent congestion are not well researched. This research aimed to estimate the mobility benefits of ramp metering during recurrent and non-recurrent congestion. To achieve the research goal, the study evaluated the effects of ramp metering on travel time reliability, the impact of ramp metering on traffic conditions upstream of a crash location, and the effects of ramp metering on traffic conditions during rainy weather. The research used data collected when ramp metering signals (RMSs) are activated and during unintentional RMSs’ downtime to account for the limitations of the conventional methods for estimating benefits. Results of the analysis focusing on recurrent congestion showed that ramp metering significantly improves travel time reliability. It was estimated that ramp metering increased travel time reliability by 23% during moderate recurrent congestion and by 28% during severe recurrent congestion. The analysis during non-recurrent congestion showed that ramp metering has varying impacts on traffic conditions upstream of a crash location. Ramp metering significantly affected traffic conditions upstream of a crash location during peak periods and daytime off-peak periods. Activating RMSs during rain in daytime off-peak periods and peak periods positively affected traffic conditions downstream of the entrance ramps. Based on the estimated benefits, agencies could establish criteria for selecting when and which RMSs to be activated to alleviate non-recurrent congestion. The estimated benefits could also be used when assessing the cost-effectiveness of future deployment of RMSs.

This study applies high-resolution archived transit data to study the effect of roadway changes using data collected before and after the completion of a project affecting transit. Methodologies are presented to compare percentile and time-of-day performance measures before and after the project. In addition, differences in travel time and travel-time variability are examined over the altered route. The case study examines one heavily used route in Portland that was recently diverted onto a newly built transit-only bridge to examine the claims that travel times would decrease and reliability would increase. The results of this study indicate that travel times increased for the majority of trips but travel-time variability during the peak period was sharply reduced.

Dynamic accessibility: Incorporating day-to-day travel time reliability into accessibility measurement

Modeling basic freeway section level-of-service based on travel time and reliability

Ramp metering has emerged as a freeway traffic management control strategy that facilitates efficient freeway operations. It has been recognized as an effective freeway management strategy that can significantly improve freeway capacity utilisation as well as increase overall network performance. However, despite these potential benefits, a number of limitations and challenges in developing ramp metering systems have been revealed from the literature. These include equity between road users, performance during over-saturated condition, real-time responsiveness and the challenges of a decentralised control approach. This thesis aims to develop and evaluate the proposed multi-agent based ramp metering algorithm and investigate some important aspects arising from the implementation of ramp metering systems. Agent technologies were introduced to model an individual ramp controller that would react dynamically under real-time traffic information. The agent concept of cooperation and collaboration was applied to develop a decentralised ramp metering system to balance system-wide efficiency and equity between road users. The proposed multi-agent ramp metering control was modelled as a hierarchical multi-agent control system comprising three operational control levels - local, group, and region. Each controller was modelled as an individual agent. At each control level, different goals and principles in metering rate calculation were applied. The developed multi-agent ramp metering algorithm (AGENT) was comparatively evaluated with other control algorithms including no control, time-of-day plan, ALINEA, FLOW and Stratified Zone under recurring and non-recurring traffic congestion. The developed Pacific Motorway traffic simulation model was applied as the study test-bed for evaluation of the algorithm performances. Under recurring traffic congestion, AGENT was found to perform very well under both normal (100 percent) and heavy (120 percent) traffic demand. Under normal traffic demand, it was found that AGENT was obviously superior to FLOW and Stratified Zone, but slightly worse than ALINEA (1.6 percent less on average flow) in terms of overall network performance. However, for on-ramp performance, AGENT was found to increase on-ramp flow by 20 percent compared to ALINEA. It was also found that AGENT generated the lowest average travel time, lowest travel delay and least on-ramp waiting time, all of which are the most important indices in term of network equity. The benefit of AGENT was found when traffic demand increased (heavy demand). It was found from the experiment that AGENT was considerably superior to ALINEA (1.7 percent higher on average flow) and other algorithms. In term of equity, AGENT was found to have the ability to balance network efficiency and equality by producing high network efficiency and excellent equity to the road users. According to the experiments under non-recurring traffic congestion regardless of severity and duration of incident, AGENT was found to have superior performance compared to other control strategies, including ALINEA. In addition, it can significantly recover the impact from incident faster than other algorithms. Its network-wide benefits over those of ALINEA, FLOW and Stratified Zone are up to 1.3, 11 and 16.1 percent on the average flow, up to 2.7, 38.7 and 67.9 percent on average speed and up to 7.4, 20.9 and 17.1 percent on average delay, respectively. For the on-ramp performance, the benefits of AGENT over ALINEA and FLOW can be found from the improvement on average on-ramp flow by 20.3, 2.4 percent and the reduction on average on-ramp delay by 1.8, 33.9 percent, respectively. Compared with Stratified Zone, AGENT was found to increase average on-ramp speed by up to 16 percent. These combined findings clearly demonstrate the potential of a multi-agent ramp metering algorithm in enhancing freeway performance and reducing congestion. This thesis has successfully achieved its stated objectives by demonstrating the feasibility of applying a decentralised approach to a ramp metering control system using a multi-agent concept and evaluating its performance against other well-known ramp metering algorithms. The study also successfully achieved its secondary objectives which included advancing the state of knowledge in the calibration and validation of traffic simulation model for freeway network; enhancing the procedures for ramp metering rate determination; formulating methodologies and frameworks for determining and calibrating parameters for ramp metering algorithm, and formulating general methodologies and frameworks that can be used to evaluate the performance of Intelligent Transport Systems applications.

Providing attractive and efficient public transport services is of crucial importance due to higher demands for mobility and the need to reduce air pollution and to save energy. The classical planning process in public transport tries to achieve a reasonable compromise between service quality for passengers and operating costs. Service quality mostly considers quantities like average travel time and number of transfers. Since daily public transport inevitably suffers from delays caused by random disturbances and disruptions, robustness also plays a crucial role. While there are recent attempts to achieve delay-resistant timetables, comparably little work has been done to systematically assess and to compare the robustness of transport plans from a passenger point of view. We here provide a general and flexible framework for evaluating public transport plans (lines, timetables, and vehicle schedules) in various ways. It enables planners to explore several trade-offs between operating costs, service quality (average perceived travel time of passengers), and robustness against delays. For such an assessment we develop several passenger-oriented robustness tests which can be instantiated with parameterized delay scenarios. Important features of our framework include detailed passenger flow models, delay propagation schemes and disposition strategies, rerouting strategies as well as vehicle capacities. To demonstrate possible use cases, our framework has been applied to a variety of public transport plans which have been created for the same given demand for an artificial urban grid network and to instances for long-distance train networks. As one application we study the impact of different strategies to improve the robustness of timetables by insertion of supplement times. We also show that the framework can be used to optimize waiting strategies in delay management.