Exploring Data Driven Models of Transit Travel Time and Delay

Transit travel time and operating speed influence service attractiveness, operating cost, system efficiency and sustainability. The Tri-County Metropolitan Transportation District of Oregon (TriMet) provides public transportation service in the tri-county Portland metropolitan area. TriMet was one of the first transit agencies to implement a Bus Dispatch System (BDS) as a part of its overall service control and management system. TriMet has had the foresight to fully archive the BDS automatic vehicle location and automatic passenger count data for all bus trips at the stop level since 1997. More recently, the BDS system was upgraded to provide stop-level data plus 5-second resolution bus positions between stops. Rather than relying on prediction tools to determine bus trajectories (including stops and delays) between stops, the higher resolution data presents actual bus positions along each trip. Bus travel speeds and intersection signal/queuing delays may be determined using this newer information. This thesis examines the potential applications of higher resolution transit operations data for a bus route in Portland, Oregon, TriMet Route 14. BDS and 5-second resolution data from all trips during the month of October 2014 are used to determine the impacts and evaluate candidate trip time models. Comparisons are drawn between models and some conclusions are drawn regarding the utility of the higher resolution transit data. In previous research inter-stop models were developed based on the use of average or maximum speed between stops. We know that this does not represent realistic conditions of stopping at a signal/crosswalk or traffic congestion along the link. A new inter-stop trip time model is developed using the 5-second resolution data to determine the number of signals encountered by the bus along the route. The variability in inter-stop time is likely due to the effect of the delay superimposed by signals encountered. This newly developed model resulted in statistically significant results. This type of information is important to transit agencies looking to improve bus running times and reliability. These results, the benefits of archiving higher resolution data to understand bus movement between stops, and future research opportunities are also discussed

Prediction of Bus Travel Time on Urban Routes without Designated Bus Stops in Makurdi Town, Benue State, Nigeria

The lack of information on bus travel time in Makurdi town to enable trip makers plan for journeys is seen as a challenge in recent times. This study developed a multiple linear regression model for predicting bus travel time along bus routes in Makurdi town. Specifically, the study assessed bus travel time on routes without designated bus stops, examined geometric features of bus routes, assessed bus dwell time and travel speeds in a heterogeneous traffic stream on routes in Makurdi town. It developed and validated a model for the bus travel time. Field survey focused on the major bus routes in Makurdi town which included; High Level roundabout to School of Remedial Studies junction (HL-SRS), High Level roundabout to Federal Medical Centre junction (HL–FMC), Wurukum roundabout to Coca Cola Complex (W-CCC) and Wurukum roundabout to Welfare Quarters junction (W–WQ). Independent parameters examined on the sites for model development included; bus route length, bus travel speed, average dwell time at random stops for pick-up and alighting of passengers, bus headway, the total number of cross and Tee intersections along the bus route, volume of motorcycles, private cars and trucks in the traffic stream, while the dependent variable was bus travel time. Based on the built model, 15 minutes approximately was established as the average bus travel time for all bus routes in Makurdi town assuming all other variables have zero magnitude. Goodness of fit test of the model yielded significant value for coefficient of determination (R2 = 0.952) and the use of Artificial Neural Network (ANN) method for validating the model also confirmed it accuracy at 93% approximately. It was therefore concluded that, bus travel time on major routes in Makurdi town could be accurately estimated using the built multiple linear regression model provided all essential input parameters of the model are used. The establishment of designated bus stops along bus routes within Makurdi town to minimise bus dwell frequency and for accurate estimation of bus travel time, as well as erection of travel information bill boards along bus routes stating average bus travel time to inform commuters that have high value of travel time were recommended

Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies

Travel Time Prediction under Mixed Traffic Conditions Using RFID and Bluetooth Sensors

Travel time information is an integral part in various ITS applications such as Advanced Traveler Information System, Advanced Traffic Management Systems etc. Travel time data can be collected manually or by using advanced sensors. In this study, suitability of Bluetooth and RFID (Radio Frequency Identifier) sensors for data collection under mixed traffic conditions as prevailing in India is explored. Reliability analysis was carried out using Cumulative Frequency Diagrams (CFDs) and buffer time index along with evaluation of penetration rate and match rate of RFID and Bluetooth sensors. Further, travel time of cars for a subsequent week was predicted using the travel time data obtained from RFID sensors for the present week as input in ARIMA modeling method. For predicting the travel time of different vehicle categories, relationships were framed between travel time of different vehicle categories and travel time of cars determined from RFID sensors. The stream travel time was then determined considering the travel time of all vehicle categories. The R-Square and MAPE values were used as performance measure for checking the accuracy of the developed models and were closer to one and lower respectively, indicating the suitability of the RFID sensors for travel time prediction under mixed traffic conditions. The developed estimation schemes can be used as part of travel time information applications in real time Intelligent Transportation System (ITS) implementations

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Groundwater transit time distribution and mean from streambed sampling in an agricultural coastal plain watershed, North Carolina, USA

We measured groundwater apparent age (s) and seepage rate (v) in a sandy streambed using point-scale sampling and seepage blankets (a novel seepage meter). We found very similar MTT estimates from streambed point sampling in a 58 m reach (29 years) and a 2.5 km reach (31 years). The TTD for groundwater discharging to the stream was best fit by a gamma distribution model and was very similar for streambed point sampling in both reaches. Between adjacent point-scale and seepage blanket samples, water from the seepage blankets was generally younger, largely because blanket samples contained a fraction of ‘‘young’’ stream water. Correcting blanket data for the stream water fraction brought s estimates for most blanket samples closer to those for adjacent point samples. The MTT estimates from corrected blanket data were in good agreement with those from sampling streambed points adjacent to the blankets. Collectively, agreement among age-dating tracers, general accord between tracer data and piston-flow model curves, and large groundwater age gradients in the streambed, suggested that the piston flow apparent ages were reasonable estimates of the groundwater transit times for most samples. Overall, our results from two field campaigns suggest that groundwater collected in the streambed can provide reasonable estimates of apparent age of groundwater discharge, and that MTT can be determined from different agedating tracers and by sampling with different groundwater collection devices. Coupled streambed point measurements of groundwater age and groundwater seepage rate represent a novel, reproducible, and effective approach to estimating aquifer TTD and MTT

An Assessment of Historical Traffic Forecast Accuracy and Sources of Forecast Error

Transportation infrastructure improvement projects are typically huge and have significant economic and environmental effects. Forecasts of demand of the facility in the form of traffic level help size the project as well as choose between several alternatives. Inaccuracy in these forecasts can thus have a great impact on the efficiency of the operational design and the benefits accrued from the project against the cost. Despite this understanding, evaluation of traffic forecast inaccuracy has been too few, especially for un-tolled roads in the United States. This study, part of a National Cooperative Highway Research Program (NCHRP) funded project, bridges this gap in knowledge by analyzing the historical inaccuracy of the traffic forecasts based on a database created as part of the project. The results show a general over-prediction of traffic with actual traffic deviating from forecast by about 17.29% on an average. The study also compares the relative accuracy of forecasts on several categorical variables. Besides enumerating the error in forecasts, this exploration presents the potential factors influencing accuracy. The results from this analysis can help create an uncertainty window around the forecast based on the explanatory variables, which can be an alternate risk analysis technique to sensitivity testing

DEVELOPMENT OF A DATABASE FOR RAPID APPROXIMATION OF SPACECRAFT RADIATION DOSE DURING JUPITER FLYBY

Interplanetary and deep space missions greatly benefit from the utilization of gravitational assists to reach their final destinations. By closely “swinging by” a planet, a spacecraft can gain or lose velocity or change directions without requiring any expenditure of propulsion. In today’s budget-driven design environment, gravity assist flybys reduce the need for on-board fuel and propulsion systems, thereby reducing overall cost, increasing payload and mission capacity, increasing mission life, and decreasing travel time. It is expected that many future missions will also be designed to swing by Jupiter in order to utilize a gravity assist. However, there is a risk associated with choosing to flyby Jupiter: increased exposure to radiation. Exposure to radiation can severely impact spacecraft electronic systems. Since today’s spacecraft consist of sophisticated circuits that operate at low voltages and currents, the effects of radiation have become increasingly important. Harsh radiation environments can have damaging effects on spacecraft electronics that may ultimately lead to mission failure. Historically, analysts use trapped particle environment data recorded from previous missions in conjunction with the planned trajectory of their individual mission to predict radiation exposure at Jupiter. Until now, no database existed that lists potential radiation exposure for a variety of possible Jupiter flyby trajectories. This thesis and associated tools allow radiation dose to be more easily determined during preliminary mission planning. Over 16,000 potential Jupiter flyby trajectories were generated via the Program to Optimize Simulated Trajectories (POST). These trajectories were then input into the European Space Agency’s (ESA) Space Environment Information System (SPENVIS) to predict the total radiation dose behind 3 mm of Aluminum shielding. SPENVIS is web-based software that has stored trapped particle models for Jupiter. Once run through SPENVIS, total flyby radiation dosage was stored for each trajectory, and an algorithm was developed that allows for interpolation and approximation of dose for cases not in the original database. This algorithm should be useful to future space mission designers who are looking to utilize a gravity assist at Jupiter and will allow a quick comparison of multiple mission scenarios with respect to flyby radiation dose