Incorporating Reliability Performance Measures into Operations and Planning Modeling Tools: Application Guidelines (2014)

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

11 The travel time reliability analysis framework incorporates two essential tools that provide the capability to produce reliability performance measures as output from operational planning and simulation models. The Scenario Manager, an integral com- ponent of the overall analytical framework, captures external unreliability sources such as special events, adverse weather, and work zones, and generates appropriate files as input into simulation models. The other key analysis tool is the vehicle Trajec- tory Processor, which calculates and visualizes travel time distributions and associated reliability indicators (such as 95th percentile travel time, Buffer Time Index, Planning Time Index, frequency that congestion exceeds some threshold) at link, path, O-D, and network levels. The travel time distributions and associated indicators are derived from individual vehicle trajectories, defined as a sequence of geographic positions (nodes) and associ- ated passage times. These trajectories are obtained as output from particle-based micro- scopic or mesoscopic simulation tools. Such trajectories may alternatively be obtained directly through measurement [e.g., global positioning system (GPS)–equipped probe vehicles], thus enabling validation of travel time reliability metrics generated on the basis of output from simulation tools. Note that both the Scenario Manager and the Trajectory Processor have been developed at a prototype level of detail and functionality for project team use only and are shared with the developer and user community on an “as is” basis. For this reason, they may not meet all requirements of an implementing agency without further development. A prerequisite for the use of these analysis tools is the availability of a particle- based traffic simulation model, capable of producing vehicle trajectory output. It is further assumed that the simulation model is fully calibrated to reasonably simulate 4 ANALYSIS TOOLS AND DATA

12 A LEXICON FOR CONVEYING TRAVEL TIME RELIABILITY INFORMATION traffic flows. For demonstration purposes, the Scenario Manager and Trajectory Processor prototypes incorporate interfaces to the Aimsun and DYNASMART-P simu- lation platforms, as examples of microscopic and mesoscopic tools, respectively. SCENARIO MANAGER The Scenario Manager is essentially a preprocessor of simulation input files for cap- turing exogenous sources of travel time variation. Recognizing the importance of the scenario definition and the complexity of identifying relevant exogenous sources, the Scenario Manager provides the ability to construct scenarios that entail any mutu- ally consistent combination of external events. These may be both demand- and supply-related events, including different traffic control plans which may be deployed under certain conditions. Accordingly, it captures parameters that define external sources of unreliability (such as special events, adverse weather, and work zones) and enables users either to specify scenarios with particular historical significance or policy interest, or to generate them randomly given the underlying stochastic processes with specific characteristics (parameters) following a particular experimental design. The built-in Monte Carlo sampling functionality allows the Scenario Manager to generate hypothetical scenarios for analysis and design purposes. When used in that manner (i.e., in random generation mode), the Scenario Manager becomes the primary platform for conducting reliability analyses, as experiments are conducted to replicate certain field conditions under both actual and hypothetical (proposed) network and control scenarios. In particular, the Scenario Manager enables execution of experi- mental designs that entail simulation over multiple days, thus reflecting daily fluctua- tions in demand, both systematic and random. The Scenario Manager also allows users to manage the conduct of reliability analyses by providing an environment for storage and retrieval of previously generated scenarios, through a scenario library approach. The scenario management function- ality allows retrieval of historically occurring scenarios or of previously constructed scenarios as part of a planning exercise (e.g., in conjunction with emergency prepared- ness planning). Given a particular scenario, the Scenario Manager’s main function is to prepare input files for microscopic or mesoscopic simulation models. In addition, the Scenario Manager can facilitate direct execution of the simulation software for a par- ticular scenario by creating the necessary inputs that reflect the scenario assumptions. An especially important and interesting feature of a well-configured Scenario Manager is that it can be tied into an area’s traffic and weather monitoring system(s). In that way, particular scenario occurrences could be stored when they materialize, with all applicable elements that define that scenario, especially demand characteristics and traffic control plans triggered for that scenario. For example, if Houston experi- ences major rainfall with extensive flood-like conditions, that scenario could be stored in terms of the event and the exogenous parameter values. Using a properly configured Scenario Manager interfaced with the data warehousing system at a given traffic man- agement center, the system operator could extract the relative occurrence probabilities and distribution functions, which would then allow calibration of these external event

13 A LEXICON FOR CONVEYING TRAVEL TIME RELIABILITY INFORMATION scenarios to actual observations. Considerable sophistication and functionality could be introduced in such a process over time—as the historical data records increase in quantity, quality, and completeness—and allow robust estimation of occurrence prob- abilities of otherwise infrequent events. TRAJECTORY PROCESSOR The vehicle Trajectory Processor is introduced to extract reliability-related measures from the vehicle trajectory output of the simulation models. It produces and helps visualize reliability performance measures (travel time distributions, indicators) from observed or simulated trajectories. Independent measurements of travel time at link, path, and O-D levels can be extracted from the vehicle trajectories, allowing for the construction of the travel time distribution. From the system operator’s perspective, reliability performance indicators for the entire system allow comparison of different network alternatives and policy and oper- ational scenarios. This could facilitate decision making in regard to actions intended to control reliability and evaluation of system performance. Reliability measures (such as 95th percentile travel time, Buffer Time Index, Planning Time Index, frequency that congestion exceeds some expected threshold) can be derived from the travel time dis- tribution or, alternatively, computed directly from the travel time data. In addition to the reliability performance indicators, it is essential to reflect the user’s point of view, as travelers will adjust their departure time, and possibly other travel decisions, in response to unacceptable travel times and delays in their daily com- mutes. User-centric reliability measures describe user-experienced or perceived travel time reliability, such as probability of on-time arrival, schedule delay, and volatility and sensitivity to departure time. In particular, to quantify user-centric reliability mea- sures, the experienced travel time and the departure time of each vehicle are extracted from the vehicle trajectory. By comparing the actual and the preferred arrival time, the probability of on-time arrival can be computed. DATA REQUIREMENTS This section provides a brief discussion of the types of data needed to implement the proposed reliability analysis framework. This discussion assumes that a base simula- tion model is already developed and properly validated, and it focuses on (a) data required for the development of scenarios for reliability analysis, and (b) data required to refine/adapt the simulation model and/or to perform travel time reliability analysis based on observed congestion conditions. As indicated, numerous external factors can affect variations in travel time. To consider these factors in the comprehensive methodology, extensive background data are required. These include collision data, weather data, and event data encompass- ing lane closures, work zones, and other incidents affecting normal traffic flow. In addition, historical vehicle traffic volumes and background travel demand for other scenarios are important for simulating events that may cause changes in travel patterns

14 A LEXICON FOR CONVEYING TRAVEL TIME RELIABILITY INFORMATION or the overall level of traffic demand. Desirable data also include trajectory data from GPS or other probe vehicle sources. These data can be processed to provide valuable information regarding actual trip travel times (portions of trips) through the study area, thus allowing comparisons to simulated data. Data for Scenario-Based Analysis The reliability analysis framework addresses a number of sources of travel time vari- ability under both recurring and nonrecurring congestion conditions, whether these af- fect the demand or supply side of the transportation system, in a random or systematic manner, endogenously or exogenously to the involved modeling tools. In general, data are needed to set parameters for the factors that will be captured endogenously in the models, whether on the demand or supply side of the system. For example, speed, flow, and occupancy data can be used to describe characteristics relevant to flow breakdown conditions (jam density, and so forth); location, time, and pricing applicable by vehicle class and type [truck, bus, high-occupancy vehicle (HOV), single-occupancy vehicle (SOV)] are needed to incorporate dynamic pricing schemes; event logs and observed or estimated compliance rates may also be needed to capture user responses to information and control measures. For the proposed scenario-based analysis in particular, data are needed to gener- ate scenarios for factors causing travel time variability due to supply-side changes that need to be addressed exogenously to the models through the Scenario Manager. Such data should include information about incidents (ideally including severity of incident and length of time), special events (type, location, time/date, duration), weather condi- tions, and work zones. In addition, before-after studies for major planned events can be helpful. Similarly, and depending on the scenarios to be addressed in the reliability analysis, data are needed for the Scenario Manager to address demand-side changes (e.g., attendance at a special event, visitors to a special place, or closure of alternative modes). Table 4.1 provides a summary of data that could be used to generate scenarios for certain exogenous factors. Such data are typically available through the transportation authorities that manage, control, or simply monitor transportation systems in an area, or through other third parties (e.g., meteorological service for weather conditions) if additional detail is needed for modeling purposes.

15 A LEXICON FOR CONVEYING TRAVEL TIME RELIABILITY INFORMATION TABLE 4.1. TYPICAL DATA REQUIREMENTS FOR DEVELOPMENT OF SCENARIOS FOR TRAVEL TIME RELIABILITY ANALYSIS Event Type Data Requirements Incident • Type (e.g., collision, disabled vehicle) • Location • Date, time of occurrence, and time of clearance • Number of lanes/shoulder and length of roadway affected • Severity in case of collision (e.g., damage only, injuries, fatalities) • Weather conditions • Traffic data in the area of impact before and during the incident (e.g., traffic flows; speed, delay, travel time measurements; queues; and other performance measures or observations, if available) Work zone • Work zone activity (e.g., maintenance, construction) that caused lane/road closure, and any other indication of work zone intensity • Location and area/length of roadway impact (e.g., milepost), number of lanes closed • Date, time, and duration • Lane closure changes and/or other restrictions during the work zone activity • Weather conditions • Special traffic control/management measures, including locations of advanced warning, speed reductions • Traffic data upstream and through the area of impact, before and during the work zone (e.g., traffic flows and percentage of heavy vehicles; speed, delay, travel time measurements; queues; and other performance measures or observations, if available) • Incidents in work zone area of impact Special event • Type (e.g., major sporting event, official visit/event, parade) and name or description • Location and area of impact (if known/available) • Date, time, and duration • Event attendance and demand generation/attraction characteristics (e.g., estimates of out-of-town crowds, special additional demand) • Approach route(s) and travel mode(s) if known • Road network closures or restrictions (e.g., lane or complete road closures, special vehicle restrictions) and other travel mode changes (e.g., increased bus transit service) • Special traffic control/management measures (e.g., revised signal timing plans) • Traffic data in the area of impact before, during, and after the event (e.g., traffic flows; speed, delay, travel time measurements; queues; and other performance measures or observations, if available) Weather • Weather station identification or name (e.g., KLGA for the automated surface observing system station at LaGuardia Airport, New York) • Station description (if available) • Latitude and longitude of the station • Date, time of weather record (desirable data collection interval: 5 minutes) • Visibility (miles) • Precipitation type (e.g., rain, snow) • Precipitation intensity (inches per hour, liquid equivalent rate for snow) • Other weather parameters (temperature, humidity, precipitation amount during previous 1 hour, if available)

16 A LEXICON FOR CONVEYING TRAVEL TIME RELIABILITY INFORMATION Trajectory Travel Time Data and Sources The specific analysis approach in the proposed reliability evaluation framework requires a special type of travel time data, which was not available until recent technological developments made its collection possible. In particular, the requirement for trajectory- based travel times for individual vehicles, which are analyzed over their time and space dimensions and various aggregate metrics, may almost exclusively be satisfied by vehicle probe-based data. Because the proposed reliability evaluation framework is based on travel times reported (and/or estimated) on a per vehicle trajectory basis, the travel time data required to support this research need to satisfy the following trajectory information requirements: • Report travel times by vehicle trip on a trajectory basis; at a minimum provide X-Y coordinates and time stamp at each reported location; • Capture both recurring and nonrecurring congestion on a range of road facilities (from freeways to arterial roads and possibly managed lanes); • Represent sufficient sampling and time-series to allow statistically meaningful analysis; and • Provide the ability to tie travel time data to other ancillary data for time variability sources (to allow parameterization for simulation testing purposes, as discussed earlier). Furthermore, the trajectory data should ideally possess the following general char- acteristics for travel time reliability analysis: • Capture both types of congestion (recurring and nonrecurring). • Cover the range of road facilities that may be included in the subject area analysis, from freeways to arterial roads and (possibly) managed lanes. • Allow statistically meaningful analysis of data through availability for a relatively long time period (e.g., a time frame that is long enough to cover seasonal variation). • Provide travel time at disaggregated levels (e.g., vehicle travel time) and at fine time intervals (e.g., link/path travel time for every 5 minutes), in addition to aver- age travel times, to capture time-of-day variation and vehicle-to-vehicle variation. • Provide sufficient information on components, causes, and other characteristics of congestion, so that appropriate parameterization can be established for simulation testing purposes. The emergence of vehicle probe data over the past few years has created the oppor- tunity to capture all necessary information for this type of analysis, since such data can be available all the time for all major roads in the network, including major arterials. Probe-based trajectory data represent a significant increase in the quality and quantity of relevant information. The detail in such data makes it possible to analyze travel time data according to network and route components (e.g., on link and path basis) as well as according to geographic aggregations (e.g., on O-D zone basis).