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From page 91... ... 89 This chapter presents a series of case studies that illustrates the application of many of the aspects of this guide. In particular, the case studies illustrate real-world examples of using a travel time reliability monitoring system (TTRMS) Read the entire page →
From page 92... ... 90 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY a prototype TTRMS at each of the five sites. These systems take in sensor data in real time from a variety of transportation networks, process these data inside a large data warehouse, and generate reports on travel time reliability to help agencies better operate and plan their transportation systems. Read the entire page →
From page 93... ... 91 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Different methodologies were applied and specific use cases were demonstrated in each case study based on each location's existing data and monitoring systems. Each case study consists of the following sections: • Monitoring system -- Detection; and -- Management systems. Read the entire page →
From page 94... ... 92 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY state-of-the-art reliability monitoring, as well as challenges in implementing guide methodologies on real data. The purpose of this case study was as follows: • Assemble regimes and travel time PDFs (TT-PDFs) Read the entire page →
From page 95... ... 93 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY On the transit side, the San Diego Metropolitan Transit System is supplying data from their real-time computer-aided dispatch system into an archived data user service. To monitor its transit fleet, the transit system has equipped more than one-third of its bus fleet with automated vehicle location transponders and more than one-half of its fleet with automated passenger count equipment. Read the entire page →
From page 96... ... 94 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY under each congested condition. This use case analysis illustrates one potential method for linking travel time variability with the sources of congestion. Read the entire page →
From page 97... ... 95 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Diego case study resource document. The demonstration of this use case concluded that the most direct analysis would be achieved by restricting the date range to dates with identical schedules. Read the entire page →
From page 98... ... 96 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Figure 4.3. Study area for Northern Virginia case study. Read the entire page →
From page 99... ... 97 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Investigations System Integration PeMS Configuration. For the purposes of this case study, data from NOVA's data collection network and management system were integrated into a developed archived data user service and TTRMS. Read the entire page →
From page 100... ... 98 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Probe Vehicle Comparisons The team performed a quality control procedure to better understand the implications of the data quality issues on travel times. In particular, the team wanted to know how well the probe data aligned with the traffic speed and travel time estimates provided by the sparsely deployed point-based detectors. Read the entire page →
From page 101... ... 99 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY distribution fit. The outputs of this method can inform travelers of the percentage chance that they will encounter moderate or severe congestion and, if they do, what their expected and 95th percentile travel times will be. Read the entire page →
From page 102... ... 100 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Figure 4.4. Study area for Sacramento–Lake Tahoe case study. Read the entire page →
From page 103... ... 101 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY for analysis as part of this project. The prerequisite data collection through monitoring system integration–related activities included ETC and Bluetooth data as described in the Sacramento–Lake Tahoe case study resource document. Read the entire page →
From page 104... ... 102 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Privacy Considerations For either of the data collection technologies described in this guide to be successful over the long term, safeguards must be put in place to ensure that the privacy of individual drivers being sampled is protected. It is recommended that any probe data collection program implemented by public agencies or private sector companies on their behalf adhere to a predetermined set of privacy principles (e.g., ITS America's Fair Information and Privacy Principles) Read the entire page →
From page 105... ... 103 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Monitoring System Detection In the Atlanta region, the Georgia Department of Transportation (GDOT collects data from over 2,100 roadway sensors, which include a mix of video detection sensors and radar detectors. Both types of sensors consist of single devices that monitor traffic across multiple lanes. Read the entire page →
From page 106... ... 104 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Management Systems GDOT monitors traffic in the Atlanta metropolitan area in real-time through Navigator, its advanced traffic management system (ATMS) Read the entire page →
From page 107... ... 105 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY by the differences in locations between the agency-owned infrastructure and the midpoint of its associated third-party link (defined by traffic message channel ID) Read the entire page →
From page 108... ... 106 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY The main objectives of the New York/New Jersey case study were as follows: • Obtain time-of-day travel time distributions for a study route based on probe data. • Identify the cause of bimodal travel time distributions on certain links. Read the entire page →
From page 109... ... 107 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY opposed to infrastructure-based sensors, which measure traffic only at discrete points. This probe data set was analyzed at two levels: at the individual GPS trace level and through aggregation into single per link speed values. Read the entire page →
From page 110... ... 108 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY the distribution until a speed distribution for the entire route is obtained. Trips are grouped by time of day, at an hourly granularity when the data density allows. Read the entire page →
From page 111... ... 109 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY individual vehicle travel times and determining how to relate these regimes to systemlevel information on average travel times. Because individual vehicle travel times on freeways are not available in the San Diego metropolitan region, data from the Berkeley Highway Laboratory (BHL) Read the entire page →
From page 112... ... 110 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Investigations System Integration Data from the BHL section of I-80 was used in this case study. This section is valuable because it has colocated dual-loop detectors and Bluetooth sensors. Read the entire page →
From page 113... ... 111 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY • Overall highway system. Operators of the roadway system (supply) Read the entire page →
From page 114... ... 112 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY TABLE 4.3. USE CASES FOR A TRAVEL TIME RELIABILITY MONITORING SYSTEM Category Subgroup Use Case System administrators and planners Administrators AE1: See what factors affect reliability AE2: Assess the contributions of the factors AE3: View the travel time reliability of a subarea AE4: Assist planning and programming decisions AE5: Document agency accomplishments AE6: Assess progress toward long-term reliability goals AE7: Assess the reliability impact of a specific investment Planners AP1: Find the facilities with highest variability AP2: Assess the reliability trends over time for a route AP3: Assess changes in the hours of unreliability for a route AP4: Assess the sources of unreliability for a route AP5: Determine when a route is unreliable AP6: Assist rural freight operations decisions Roadway network managers and users Managers MM1: View historical reliability impacts of adverse conditions MM2: Be alerted when the system is struggling with reliability MM3: Compare a recent adverse condition with prior ones MM4: Gauge the impacts of new arterial management strategies MM5: Gauge the impacts of new freeway management strategies MM6: Determine pricing levels using reliability data Drivers -- constrained trips MC1: Understand departure times and routes for a trip MC2: Determine a departure time and route just before a trip MC3: Understand the extra time needed for a trip MC4: Decide how to compensate for an adverse condition MC5: Decide en route whether to change routes Drivers -- unconstrained trips MU1: Determine the best time of day to make trip MU2: Determine how much extra time is needed Transit system Transit planners TP1: Determine routes with the least travel time variability TP2: Compare exclusive bus lanes with mixed-traffic operations Transit schedulers TS1: Acquire reliability data for building schedules TS2: Choose departure times to minimize arrival uncertainty Transit operators TO1: Identify routes with the poorest reliability TO2: Review reliability for a route TO3: Examine the potential impacts of bus priority on a route TO4: Assess a mitigating action for an adverse condition Transit passengers TC1: Determine the on-time performance of a trip TC2: Determine an arrival time just before a trip TC3: Determine a friend's arrival time TC4: Understand a trip with a transfer Freight system Freight service providers FP1: Identify the most reliable delivery time FP2: Estimate a delivery window FP3: Identify how to maximize the probability of an on-time delivery FP4: Assess the on-time probability for a scheduled shipment FP5: Assess the impacts of adverse highway conditions FP6: Determine the start time for a delivery route FP7: Find the departure time and routing for a set of deliveries FP8: Solve the multiple vehicle routing problem under uncertainty FP9: Alter delivery schedules in real time Freight customers FC1: Minimize shipping costs due to unreliability FC2: Determine storage space for just-in-time deliveries FC3: Find the lowest-cost reliable origin FC4: Find the warehouse site with the best distribution reliability Read the entire page →
From page 115... ... 113 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY SEE WHAT FACTORS AFFECT RELIABILITY (AE1) In this use case, the agency administrator wants to see what factors affect the reliability of the segments and routes in the system. Read the entire page →
From page 116... ... 114 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Step 4 is to label each observation -- all 216,000 in this case -- in terms of the regime that was operative for each observation. The technique for adding these labels involves two substeps. Read the entire page →
From page 117... ... 115 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Figure 4.9. Five-minute average weekday travel rates for three routes in San Diego. Read the entire page →
From page 118... ... 116 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY 40 60 80 100 120 140 160 180 200 0 20000 40000 60000 80000 100000 120000 140000 160000 Tr av el R at es (s ec /m i) VMT/hour Trends in Travel Rates versus VMT/Hour for the I-5 Route 40 60 80 100 120 140 160 180 200 0 20000 40000 60000 80000 100000 120000 140000 160000 Tr av el R at es (s ec /m i) Read the entire page →
From page 119... ... 117 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Appendix D contains detailed descriptions of how the data were categorized into each nonrecurring event. The second substep in Step 4 involves labeling each observation based on the nominal loading of the system expected for each observation. Read the entire page →
From page 120... ... 118 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Although no right answer exists for the number of congestion categories to use, four were selected here: uncongested, low, moderate, and high. Uncongested meant the SV was below 20; low meant 20 to 40; moderate, 40 to 120; and high, above 120. Read the entire page →
From page 121... ... 119 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Figure 4.12. CDFs by regime for the three routes in San Diego. Read the entire page →
From page 122... ... 120 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Steps 1 through 4 are the same as for Use Case AE1. Step 5 aims to determine the extent to which the facilities are affected by various factors. Read the entire page →
From page 123... ... 121 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY and weather) during high congestion largely overlap, and no one CDF dominates the other. Read the entire page →
From page 124... ... 122 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY sum–product of the SV and n values. The far-right column (Facility Total) Read the entire page →
From page 125... ... 123 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY seems apparent that its problems are due to the geometric conditions on the section of SR-163 from I-805 to I-5. All three routes are significantly affected by high congestion, even under normal conditions; the TR-CDF for that condition is dramatically different from the CDFs for normal operation under less-congested conditions. Read the entire page →
From page 126... ... 124 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Step 1 is to select the subarea of interest. In this case, it is the same portion of the San Diego metropolitan area shown in Figure 4.8. Read the entire page →
From page 127... ... 125 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY As shown in Table 4.9, normal conditions (nonrecurring event category) under high congestion are the principle source of unreliability. Read the entire page →
From page 128... ... 126 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Step 2 is to assemble TR-PDFs for routes and areas for before-and-after traffic conditions under equivalent operating conditions. In the context of the hypothetical situation described above, this has already been done, and the results are presented in Figure 4.12. Read the entire page →
From page 129... ... 127 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Figure 4.14. Using TR-CDFs to analyze performance changes. Read the entire page →
From page 130... ... 128 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Although the performance for lower percentiles in Year Z is not better than in Year Y (but better than or the same as in Year X) , it is better for the higher percentiles, at about the 88th percentile and above. Read the entire page →
From page 131... ... 129 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY Combinations that lie on the outer boundary of this plot are important conditions on which to focus. Figure 4.15 shows there are a few conditions that merit significant attention to mitigate low-probability, high-consequence events. Read the entire page →
From page 132... ... 130 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY This use case can be addressed using any one of the routes examined in Appendix D (I-5, SR-15, SR-163, and I-8) Read the entire page →
From page 133... ... 131 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY TABLE 4.13. HOURS OF UNRELIABLE OPERATION BY REGIME AND FACILITY Route Condition Normal Demand Weather Special Events Incidents Total HoursSV Hours SV Hours SV Hours SV Hours SV Hours I-5 Uncongested 7 0 60 0 46 0 111 11 172 24 35 High 205 1,065 1,415 39 2,563 15 1,399 9 1,769 39 1,167 SR-15 Uncongested 15 0 47 0 68 0 29 0 139 5 5 Low 27 0 118 9 106 16 0 0 97 0 25 Moderate 46 0 127 1 151 23 0 0 93 0 24 High 241 1,160 2,415 55 3,751 14 3,113 14 3,032 49 1,292 SR-163 Uncongested 11 0 13 0 61 0 21 0 54 0 0 Moderate 56 0 169 43 399 28 601 29 684 30 129 High 261 1,064 1,789 86 1,924 21 1,424 20 1,385 80 1,271 I-8 Westbound Uncongested 5 0 16 0 27 5 0 0 17 0 0 Low 9 0 21 0 101 51 20 0 24 0 51 Moderate 11 0 35 0 80 0 473 3 337 10 13 High 45 0 50 0 1,180 15 0 0 805 20 35 TABLE 4.14. Read the entire page →
From page 135... ... 133 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY can see that as incidents occur or as the system becomes more heavily loaded, the 5th percentile travel time increases. The first event occurs at about 11:00, when the travel times abruptly rise and then return to normal. Read the entire page →
From page 136... ... 134 GUIDE TO ESTABLISHING MONITORING PROGRAMS FOR TRAVEL TIME RELIABILITY travel times achieved. People in the less-affected lanes are able to achieve significantly shorter travel times than those in the more-affected lanes. Read the entire page →

From page 91...

... 89 This chapter presents a series of case studies that illustrates the application of many of the aspects of this guide. In particular, the case studies illustrate real-world examples of using a travel time reliability monitoring system (TTRMS)