Transportation Planning Featured Project: Modeling Traffic Impacts on Visitor Experience and on Wildlife in Public Spaces

Principal Investigator: Max Donath, ITS Institute Co-investigators: Ted Morris & John Hourdos, Minnesota Traffic Observatory

Faced with increasing visitation and pressure to defend or change the limits to road traffic, managers of public spaces need to develop a greater understanding of the impacts of traffic volume and traffic patterns on the physical, biological, and social environment of roads through remote public spaces. The challenges facing the National Park Service in Denali National Park in Alaska exemplify this situation. Every year, hundreds of thousands of visitors travel to the park to experience unparalleled wildlife viewing within the unspoiled Alaska wilderness. To protect the park’s natural resources as well as the visitor experience, private vehicle traffic on the park’s only road is heavily restricted; for most visitors, the only way to experience much of the park is by booking a seat on one of the park’s special tour buses or purchasing a ticket to ride on a park-sponsored visitor shuttle bus, which carry them to different destinations along a largely unpaved 94-mile road, as shown in Figure 1 below.

Figure 1. Map of Denali Park Road. Red arrows indicate destination points along the route.

Figure 1. Map of Denali Park Road. Red arrows indicate destination points along the route.

But as the number of annual visits to the park has increased, continuing a trend that began more than 30 years ago with the opening of the George Parks Highway that connects the park to Anchorage and Fairbanks (right side of map), park managers have found themselves under pressure to determine if the currently enforced trip limits of 10,512 vehicle trips along the road can be increased without spoiling the visitor experience or negatively impacting the very wildlife that people wish to see. To address this question, the MTO teamed with the University of Vermont’s Park Studies Laboratory as well as biologists and park management from Denali.

The MTO developed a model to simulate the complex relationships between traffic patterns and wildlife movements in the park. The model integrates indicators of impact on visitor experience and wildlife, and traffic logistic considerations together. The model is now being used by park managers to answer the kind of “what-if” questions about park road use that would be impossible to test in the real world without risk of degrading impacts on visitor experience and wildlife. Results from a recently completed study of a set of scenario cases are presented below.

Figure 2. Information-flow diagram of Denali Park traffic model.

Figure 2. Information-flow diagram of Denali Park traffic model.


The Model

The model is based on a traffic microsimulation, in which individual vehicles are modeled as independent entities that operate according to a set of internal rules. In this type of simulation, traffic characteristics such as speed and demand are implemented as probabilities; thus, microsimulations yield accurate pictures of traffic patterns by aggregating the results of multiple model runs. For this work, AIMSUN (Advanced Interactive Microscopic Simulator for Urban and Non-urban Networks), a commercially available traffic simulation tool was utilized.

Speeds of the buses were characterized for each operator and route by analyzing trajectories from over 4000 trips by 87 buses instrumented with onboard Automatic Vehicle Locator (AVL) GPS devices during the 2007 peak season (May 26 through Sept 13). Dwell times at designated rest stops and campsites were also extracted from this data. Map 2 represents several trips collected on a peak season day.

Figure 3. Simulation results showing vehicle speeds along Denali Park Road.

Figure 3. Simulation results showing vehicle speeds along Denali Park Road.

Accurately modeling the unique features of the Denali Park Road required special care by the research team. A geospatial database provided by the Denali park administration served as the foundation for the model. The road winds through several mountain passes, as shown in Figure 4.

Figure 4. Vehicle approaching one of several mountain passes along Denali Park Road.

Figure 4. Vehicle approaching one of several mountain passes along Denali Park Road.

After mile 30, the road narrows from two lanes to 1.5 lanes, imposing special restrictions on passing; the traffic model incorporates behavior rules based on the yielding and passing rules observed by tour operators in the park. The simulation closely reproduces actual patterns of travel delay on the park road, which depend on prevailing traffic conditions and on position along the route, as shown in Figure 5 (click to view video).

Another important aspect of the simulation design was modeling interactions with wildlife and the effects of vehicles stopping along the route for wildlife viewing. This required understanding when and where the buses stopped to see wildlife, as well as characterizing the stop duration behavior of bus operators during the wildlife encounters. Touch panel data loggers (Figure 4) developed by the MTO were installed on 20 buses to allow drivers to record when they stopped for wildlife encounters and for other reasons. These data loggers integrated directly with the buses’ onboard Automated Vehicle Locator systems. Over 5,000 entries out of approximately 60,000 stops were analyzed.

Figure 6. Touch screen data logging system mounted in cab of Denali tour bus; small images show user interface for logging wildlife encounters.

Figure 6. Touch screen data logging system mounted in cab of Denali tour bus;
small images show user interface for logging wildlife encounters.

The resulting data were used to build time-space maps of wildlife encounters, which were then integrated into the traffic simulation. If a bus encounters wildlife in the simulation, the simulation triggers an incident that forces vehicles to stop. Figure 7 shows two such maps, for bear and caribou encounters, plotted over an entire season. An actual peak-season day that represented the trends captured in these maps was extracted from this data for running any one of six different scenario conditions.

Figure 7. Time-space maps of wildlife encounters based on data collected using in-vehicle data loggers.

Figure 7. Time-space maps of wildlife encounters based on data collected using in-vehicle data loggers.


Results

Several simulation experiments were performed using the traffic model to understand how allowing more buses to travel on the road would impact visitor standards of quality (crowding) and wildlife behavior. The park management team provided five daily scheduling scenarios, each of which incrementally increased the number of buses running along the park road from a current acceptable base condition. The simulation model was run several times for each scenario to play out a typical peak day in the park.

The University of Vermont Park Studies team developed several specific crowding measures that indicated visitor experience quality. Indicators and norm standards were developed for wildlife stop viewing at any point along the road, as well as for a scenic rest stop and two scenic viewscapes. These were incorporated into the simulation model in order to evaluate the effects of the various scenarios. Results of these simulations are shown in the figures that follow.

Figure 8 shows the effect of increased traffic scenarios on wildlife viewing locations along the road, highlighting a sudden jump in buses at a specific location under increased traffic. The violation rate that exceeded a visitor standard level of ‘unacceptable’ conditions (4–5 buses) grow markedly between the 40 and 50 percent increase scenarios (click to view video).

Figure 9 shows crowding effects, in terms of the number of buses at designated scenic viewscape locations. The peak crowding times at two scenic road viewscapes separated by only five miles occurred over two hours apart. This could not have been predicted without using the model.

Figure 9. Peak crowding at selected viewscape locations. The peak crowding times at two scenic road viewscapes separated by only five miles occurred over two hours apart. The scheduled departure times for the buses for each of the six scenarios are indicated in the top graph.

Figure 9. Peak crowding at selected viewscape locations. The peak crowding times
at two scenic road viewscapes separated by only five miles occurred over two hours
apart. The scheduled departure times for the buses for each of the six scenarios
are indicated in the top graph.

Figure 10 shows the effect of increased traffic scenarios on wildlife crossing opportunities. By studying the movement patterns of Dall sheep, it was determined that a sufficient gap time between vehicles of 10 minutes or more is desired to provide ample opportunity for Dall sheep to cross the road during their migration and foraging patterns. All scenarios produced significant decreases, although all crossing points are not affected equally. The reduction of approximately two hours was of concern to park managers. Other crossing locations will be considered in future simulations in order to understand if alternative migratory paths are affected more or less than these critical crossing locations.

Figure 10. Total time available for animals to cross the Park Road (during gaps of greater than 10 minutes between vehicles) under base conditions and increased traffic scenarios.

Figure 10. Total time available for animals to cross the Park Road (during gaps
of greater than 10 minutes between vehicles) under base conditions and
increased traffic scenarios.


Future work

The traffic microsimulation model revealed that increasing scheduled bus service to provide more access could impact both the visitor experience and the park’s wildlife in unexpected ways.

This study also demonstrated how Global Positioning System (GPS) data can be used to examine and define complex interactions between drivers and the surrounding environment, as well as extraction of travel route itineraries and their travel time characteristics. It introduced a method to use such data to validate a computer based traffic microsimulation model’s ability to mimic the dynamic travel behaviors of buses and vehicles in a unique setting.

Future work will examine additional scenario case studies in order to understand the sensitivity of these findings to scheduling demands and wildlife encounters along the road. For example, the model will be used to determine if a schedule that increases service without affecting visitor standards of quality and impacts on wildlife. The park also plans on conducting a Before-After-Controlled-Impact study (BACI) utilizing an alternative service level that is guided by the simulation results. Similar data will be collected during this study period allowing the model to be further validated as well as to assess the ability of the park to monitor impact indicators of crowding and wildlife. Park management will then have a rational basis to determine proper usage levels as well as more proactively manage visitor use impacts that result from using the buses.


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Acknowledgements

The Minnesota Traffic Observatory would like to thank Laura Phillips, Tom Meier, and Phillip Hooge of Denali National Park for their assistance and support. This research is supported by Cooperative Agreement #J9836-06-0003 through the Great Lakes Northern Forest Cooperative Ecosystems Studies Unit between the University of Minnesota and the National Park Service.