Ambulance response time is a key measure used to assess EMS system performance. However, the speed with which ambulances respond to emergencies can be highly variable. In some cases, this is due to geography. In dense urban areas for example, the distances traveled are short, but traffic and other hindrances such as traffic calming measures and high rise elevators cause delays, while rural areas involve greater distances and longer travel times. There are two major components of response time: first, pre-travel delay to prepare for ambulance dispatch, and second the actual travel time to the callers location. Response time standards are often established in order to provide fast and reliable service to the most severely ill patients. Standards typically specify the percentage of time an emergency response team can get to a call within a certain time threshold. This is referred to as “coverage”. This thesis deals with the development of a new response time model that predicts not only the mean response time, but estimates its variability. The models are developed based on historical data provided by the Region of Waterloo EMS and will permit the Region to predict EMS coverage. By analyzing the historical data, we found that response times from EMS stations to geographical locations within the Region of Waterloo are characterized by lognormal distributions. For a particular station – location pair we can thus use this information to predict coverage if we are able to specify the parameters of the distribution. We do this by characterizing the travel time and pre-travel delay times separately, and then adding the two to estimate coverage. We will use a previously proposed model that estimates the mean travel time from a station to a demand point as a function of road types traversed. We also compare the results of this model with another well known model and show that the first model is suitable to apply to the Region of Waterloo. In order to estimate the standard deviation of the response time, we propose a simple but effective model that estimates the standard deviation as a function of mean response time.

As usual route planners are based on the principle of travel time minimization, the travel speed and distance from the destination are two key factors to calculate the optimal route. Current systems are based on predetermined speed limits stored on road maps. However, few of the systems used today, consider current environmental influences. The aim of this thesis is to examine the extent of route changes for MIT (Motorized Individual Transport), when weather-related speed reductions are considered. The basis for the work are road data from OpenStreetMap and historical weather data in the NetCDF (Network Common Data Format) provided by the company Ubimet. The data is processed, by using the ArcGIS software from ESRI (Environmental Systems Research Institute, Inc). The study area corresponds to the territory of Austria. After the treatment of roads and weather information, the records are processed, using the software, to weather-related road networks. This is done by two different methods, static and dynamic. The static model concerns the routing behaviour using a specific weather situation, while the dynamic model takes temporal change of the weather into account. For the analysis of the new, influenced by the weather, routing behaviour the created routes at times of strong weathering are compared with predetermined reference cases without considering the weather. Routes referring to periods of normal and strong weathering, e.g. heavy rain, are analysed and compared in travel time, route length and exposure to adverse weather conditions.

Ambulance response time is a key measure used to assess EMS system performance. However, the speed with which ambulances respond to emergencies can be highly variable. In some cases, this is due to geography. In dense urban areas for example, the distances traveled are short, but traffic and other hindrances such as traffic calming measures and high rise elevators cause delays, while rural areas involve greater distances and longer travel times. There are two major components of response time: first, pre-travel delay to prepare for ambulance dispatch, and second the actual travel time to the callers location. Response time standards are often established in order to provide fast and reliable service to the most severely ill patients. Standards typically specify the percentage of time an emergency response team can get to a call within a certain time threshold. This is referred to as “coverage”. This thesis deals with the development of a new response time model that predicts not only the mean response time, but estimates its variability. The models are developed based on historical data provided by the Region of Waterloo EMS and will permit the Region to predict EMS coverage. By analyzing the historical data, we found that response times from EMS stations to geographical locations within the Region of Waterloo are characterized by lognormal distributions. For a particular station – location pair we can thus use this information to predict coverage if we are able to specify the parameters of the distribution. We do this by characterizing the travel time and pre-travel delay times separately, and then adding the two to estimate coverage. We will use a previously proposed model that estimates the mean travel time from a station to a demand point as a function of road types traversed. We also compare the results of this model with another well known model and show that the first model is suitable to apply to the Region of Waterloo. In order to estimate the standard deviation of the response time, we propose a simple but effective model that estimates the standard deviation as a function of mean response time.

The artificial neural network (ANN) approach has been recognized as a capable technique to model the highly complex and nonlinear problem of travel time prediction. In addition to the nonlinearity, a traffic system is also temporally and spatially dynamic. Addressing the temporal-spatial relationships of a traffic system in the context of neural networks, however, has not received much attention. Furthermore, many of the past studies have not fully explored the inclusion of incident information into the ANN model development, despite that incident might be a major source of prediction degradations. Additionally, directly deriving corridor travel times in a one-step manner raises some intractable problems, such as pairing input-target data, which have not yet been adequately discussed. In this study, the corridor travel time prediction problem has been divided into two stages with the first stage on prediction of the segment travel time and the second stage on corridor travel time aggregation methodologies of the predicted segmental results. To address the dynamic nature of traffic system that are often under the influence of incidents, time delay neural network (TDNN), state-space neural network (SSNN), and an extended state-space neural network (ExtSSNN) that incorporates incident inputs are evaluated for travel time prediction along with a traditional back propagation neural network (BP) and compared with baseline methods based on historical data. In the first stage, the empirical results show that the SSNN and ExtSSNN, which are both trained with Bayesian regulated Levenberg Marquardt algorithm, outperform other models. It is also concluded that the incident information is redundant to the travel time prediction problem with speed and volume data as inputs. In the second stage, the evaluations on the applications of the SSNN model to predict snapshot travel times and experienced travel times are made. The outcomes of these evaluations are satisfactory and the method is found to be practically significant in that it (1) explicitly reconstructs the temporalspatial traffic dynamics in the model, (2) is extendable to arbitrary O-D pairs without complete retraining of the model, and (3) can be used to predict both traveler experiences and system overall conditions.

Thesis (Ph. D. in Civil and Environmental Engineering)--University of California, Berkeley, 2003. / "Fall 2003." Includes bibliographical references (p. 131-136). Also available online at the ITS Berkeley web site (www.its.berkeley.edu/publications).

This thesis describes a simplified method for estimating distances directly from terrestrial photographs. It was felt that any method devised must overcome present limitations and meet three basic criteria to be practical. These criteria are: 1) that the method require no subject-visible markings, 2) that it require no special equipment or training to use, and 3) that it provides sufficient accuracy to be useful. A number of possible methods were considered, and were evaluated against these criteria. the Accuacy ( reliablity) of the methods was tested using a variety of statistical tests. The recommended method consists of first selecting a reference distance whose length is known. This reference should be in a plane parallel to and approximately the same distance from the camera as the desired distance. It was found that a vehicle dimension such as the tire track gives consistently the best results. Both the reference and the desired distances are then scaled on the photograph and a ratio is applied to drectly estimate the desired distance. Greatest reliability is achieved when the subject is directly in fromt of, or behind the camera and at a distance between 50 and 250 feet. By following thers guidelines the stated objectives can be met by using this method.

No description available.

This report describes an assessment of using dedicated short-range communications(DSRC) technology to perform travel time monitoring and automated incident detectionon a segment of rural freeway. The assessment used the CORSIM traffic simulation toolto simulate traffic and incidents on a segment of rural freeway. Output data from thesimulation was subjected to post-processing to produce the "probe and beacon" data thatwould be produced by a DSRC-based system. An incident detection algorithm wasdeveloped, which used a travel time threshold and a counter. Travel times exceeding thethreshold incremented the counter, while travel times below the threshold decrementedthe counter (unless it was at zero). An alarm was generated when the counter reached apre-selected level. This algorithm was tested on selected data files, and the results wereused to identify the "best" values of the threshold and counter alarm level. Using these"best" values, the algorithm was then applied to the "probe and beacon" data todetermine how quickly the system could detect various traffic incidents. The analysisshowed that the system could provide rapid and reliable detection of incidents.During the simulation and analysis, several parameters were varied to observe theirimpacts on the system performance. These parameters included traffic volume, incidentseverity, percentage of vehicles with transponders, spacing of roadside readers, andlocation of the incident relative to the next downstream reader. Each parameter proved tohave a significant effect on the detection time, and the observed impacts were consistentwith logical expectations. In general, the time to detect an incident was reduced inresponse to (1) an increase in traffic volume, (2) an increase in incident severity, (3) anincrease in transponder population, (4) a reduction in reader spacing, and (5) a reductionin distance from incident location to next downstream reader.Preliminary estimates were developed of the costs associated with implementing aDSRC-based traffic monitoring system. The relationship between system cost andsystem performance was explored and illustrated.Recommendations were developed and presented. These included further analysis basedon traffic simulations, followed by a limited field deployment to validate the analysisresults.

The thesis evaluates mobility based on mobile phone positions. The aim is to develop and assess different methods for travel demand estimation based on CDR data. Besides this estimation location data in cellular data is explained in more detail and a previous work based on mobile phone data and travel demand estimation is reviewed. The different methods of travel time estimation include both static and dynamic estimation. The static travel demand estimation evaluates movements in the city based on predefined time periods, whereas the dynamic estimations are based on different definitions of a trip. A trip can be defined as movements between important places, or just simply count a trip between each position, or a filtering of active states to create more accurate origin-destination matrices. The second part of the thesis includes evaluation of travel time based on CDR data before the final conclusions are drawn. The main finding of the thesis is that it is possible to assess mobility in a city based on CDR data, even if there are no validation data available.

It is important to the future of Melbourne that its development should take the direction of the most benefit to those who live in the city and those who may live here in the future. The current patterns of urban development, which are based on use of previously undeveloped land on the fringes of the suburban area, are now being criticised as being uneconomic in terms of cost of providing infrastructure, the use of natural resources and the waste of time for residents who must travel for long distances. (For complete abstract open document)