The Importance of Being Early

The assumption that the penalty for being early is less than that for being late was put forward by Vickrey (1963) who analyzed how commuters compare penalties in the form of schedule delay (due to peak hour congestion), against penalties in the form of reaching their destination (ahead or behind their desired time of arrival). This assumption has been tested by many researchers since then for various applications, especially in modeling congestion pricing (Arnott et al., 1990) where it is critical to understand the tradeoff between schedule delay and travel delay. Key findings are summarized in the second section of this paper. This research aims to test this hypothesis of earliness being less expensive than lateness using empirical data at different levels and across different regions. New methods to estimate the ratio of earliness to lateness for different types of datasets are developed, which could be used by agencies to implement control policies like congestion pricing or other schemes more accurately. Travel survey data from metropolitan areas provide individual travel patterns while loop detector data provide link level traffic flow data.Schedule Delay, Travel Time, Traffic, Travel Behavior.

Evaluation of Coordinated Ramp Metering (CRM) Implemented By Caltrans

Coordinated ramp metering (CRM) is a critical component of smart freeway corridors that rely on real-time traffic data from ramps and freeway mainline to improve decision-making by the motorists and Traffic Management Center (TMC) personnel. CRM uses an algorithm that considers real-time traffic volumes on freeway mainline and ramps and then adjusts the metering rates on the ramps accordingly for optimal flow along the entire corridor. Improving capacity through smart corridors is less costly and easier to deploy than freeway widening due to high costs associated with right-of-way acquisition and construction. Nevertheless, conversion to smart corridors still represents a sizable investment for public agencies. However, in the U.S. there have been limited evaluations of smart corridors in general, and CRM in particular, based on real operational data. This project examined the recent Smart Corridor implementation on Interstate 80 (I-80) in the Bay Area and State Route 99 (SR-99, SR99) in Sacramento based on travel time reliability measures, efficiency measures, and before-and-after safety evaluation using the Empirical Bayes (EB) approach. As such, this evaluation represents the most complete before-and-after evaluation of such systems. The reliability measures include buffer index, planning time, and measures from the literature that account for both the skew and width of the travel time distribution. For efficiency, the study estimates the ratio of vehicle miles traveled vs. vehicle hour traveled. The research contextualizes before-and-after comparisons for efficiency and reliability measures through similar measures from another corridor (i.e., the control corridor of I-280 in District 4 and I-5 in District 3) from the same region, which did not have CRM implemented. The results show there has been an improvement in freeway operation based on efficiency data. Post-CRM implementation, travel time reliability measures do not show a similar improvement. The report also provides a counterfactual estimate of expected crashes in the post-implementation period, which can be compared with the actual number of crashes in the “after” period to evaluate effectiveness

Weighting Waiting: Evaluating the Perception of In-Vehicle Travel Time Under Moving and Stopped Conditions

This paper describes experiments comparing traditional computer administered stated preference with virtual experience stated preference to ascertain how people value stopped delay compared with stop-and- go or freeflow traffic. The virtual experience stated preference experiments were conducted using a wrap around driving simulator. The two methods produced two different results, with the traditional computer assisted stated preference suggesting that ramp delay is 1.6 Ð 1.7 times more onerous than freeway time, while the driving simulator based virtual experience stated preference suggested that freeway delay is more onerous than ramp delay. Several reasons are hypothesized to explain the differences, including recency, simultaneous versus sequential comparison, awareness of public opinion, the intensity of the stop-and-go traffic, and the fact that driving in the real-world is a goal directed activity. However without further research, which, if any, of these will eventually prove to be the reason is unclear. What is clear is that a comparison of the computer administered stated preference with virtual experience stated preference produces different results, even though both procedures strive to find the same answers in nominally identical sets of conditions. Because people experience the world subjectively, and make decisions based on those subjective experiences, future research should be aimed at better understanding the differences between these subjective methodologies.transportation, travel behavior, driving simulator, ramp meters

Effects of low speed limits on freeway traffic flow

Recent years have seen a renewed interest in Variable Speed Limit (VSL) strategies. New opportunities for VSL as a freeway metering mechanism or a homogenization scheme to reduce speed differences and lane changing maneuvers are being explored. This paper examines both the macroscopic and microscopic effects of different speed limits on a traffic stream, especially when adopting low speed limits. To that end, data from a VSL experiment carried out on a freeway in Spain are used. Data include vehicle counts, speeds and occupancy per lane, as well as lane changing rates for three days, each with a different fixed speed limit (80 km/h, 60 km/h, and 40km/h). Results reveal some of the mechanisms through which VSL affects traffic performance, specifically the flow and speed distribution across lanes, as well as the ensuing lane changing maneuvers. It is confirmed that the lower the speed limit, the higher the occupancy to achieve a given flow. This result has been observed even for relatively high flows and low speed limits. For instance, a stable flow of 1942 veh/h/lane has been measured with the 40 km/h speed limit in force. The corresponding occupancy was 33%, doubling the typical occupancy for this flow in the absence of speed limits. This means that VSL strategies aiming to restrict the mainline flow on a freeway by using low speed limits will need to be applied carefully, avoiding conditions as the ones presented here, where speed limits have a reduced ability to limit flows. On the other hand, VSL strategies trying to get the most from the increased vehicle storage capacity of freeways under low speed limits might be rather promising. Additionally, results show that lower speed limits increase the speed differences across lanes for moderate demands. This, in turn, also increases the lane changing rate. This means that VSL strategies aiming to homogenize traffic and reduce lane changing activity might not be successful when adopting such low speed limits. In contrast, lower speed limits widen the range of flows under uniform lane flow distributions, so that, even for moderate to low demands, the under-utilization of any lane is avoided. These findings are useful for the development of better traffic models that are able to emulate these effects. Moreover, they are crucial for the implementation and assessment of VSL strategies and other traffic control algorithms.Peer ReviewedPostprint (published version

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamic

Traffic at the Edge of Chaos

We use a very simple description of human driving behavior to simulate traffic. The regime of maximum vehicle flow in a closed system shows near-critical behavior, and as a result a sharp decrease of the predictability of travel time. Since Advanced Traffic Management Systems (ATMSs) tend to drive larger parts of the transportation system towards this regime of maximum flow, we argue that in consequence the traffic system as a whole will be driven closer to criticality, thus making predictions much harder. A simulation of a simplified transportation network supports our argument.Comment: Postscript version including most of the figures available from http://studguppy.tsasa.lanl.gov/research_team/. Paper has been published in Brooks RA, Maes P, Artifical Life IV: ..., MIT Press, 199

Travel Time Reliability and Level of Service

Travel time reliability (TTR) is familiar to travelers, and its indices are useful measures of the quality of a freeway’s service. Technical groups such as highway agencies are more familiar with a freeway’s level of service (LOS), but the LOS does not capture the variability in travel time. Similar to Pu (2011) this thesis introduces a modified buffer index (BI) incorporating a median rather than average travel time as a new travel time reliability measure. Current research by the SHRP 2 L08 Project Team defines freeway reliability LOS by determining equivalent travel time index for similar travel speed ranges shown on page 8 in TABLE 2. It is anticipated that the new index will be able to provide an additional model for defining LOS using TTR as a service measure on freeway corridors. Data from the BlueTOAD (Bluetooth Travel-time Origination and Destination) devices, which utilize Bluetooth technology, was used to develop a methodology to determine a LOS of the highway facility with the use of the new TTR index and a section of the I-12 highway was used for this study. The Wilcoxon Signed Ranked test was used to assess whether the difference between the median values of the new and existing BIs at various speed ranges are significant. From the Wilcoxon Signed Ranked test the difference between the new and existing BI was found to be significant for speed ranges sr; sr ≥ 60mph, 50≤sr≤59, 45≤sr≤49, 40≤sr≤44, 35≤sr≤39, but not sr \u3c 35mph. However the BI was inconclusive in providing an accurate measure for defining LOS. This is due to the data showing an increasing linear upward trend from LOS A to LOS C but then starts decreasing linearly downwards from LOS D to LOS F. More analysis is needed at all the speed ranges and should be carried out on several segments along the I-12 corridor instead of one segment in order to obtain comprehensive results. Data, including volume data for an entire year is also needed to comprehensively analyse the LOS