FIGURE: PROPOSED FRAMEWORK FOR NETWORK SCREENING

 

There are five potential scenarios for conducting network screening:

3.1. Traditional Approach (Collision-based measures)

This option can be used when conflict data are not available for the locations being analyzed. Historical crash data can be used to conduct network screening through naïve or model-based approaches. Naïve methods involve calculating crash rates for each location based on the total number of crashes that occurred in a given period. Model-based methods involve developing Safety Performance Functions (SPFs) using crash data and covariates such as traffic volume and geometric characteristics. The SPFs can then be used in combination with observed crash data (e.g., Empirical Bayes) to predict the number of crashes that are likely to occur at each location.

3.2. Competitive Data Fusion

This option can be selected when conflict data are available for the same spatial and road user type coverage. In this approach, both historical crash data and conflict data collected from point sensors (e.g., cameras, lidar) and/or mobile sensors (e.g., GPS data, CAVs) can be used to conduct safety analysis. One method involves using SSMs as covariates in crash-based SPF estimation. Another method is to separately estimate and predict the crash risk based on historical observed collision and conflict data and then use more sophisticated models such as copula models or meta-analysis models to combine the results from both measures for network screening.

3.3. Complementary Data Fusion

This option assumes that both conflict data and crash data are available, but their coverage is different. For example, crash data may only be available for specific types of road users such as vehicular collisions. In this case, conflict data can be used to estimate the risk of vulnerable road user (VRU) collisions (which are often lacking in observational data), and both conflict-based and collision-based measures can be combined to conduct network screening for all types of road users.

3.4. SSM-based Measures

This alternative can be used when crash data is unavailable, and network screening needs to be conducted based on conflict data. If conflict data is available for all locations, observed conflict measures such as PET and TTC can be used. If conflict data is only available for some locations, conflict indicators can be used to develop models to estimate and predict the number of conflicts for all locations. In this case, all the methods discussed for collision-based measures (i.e., naïve and model-based methods) can be used to conduct safety analysis.

3.5. Simulation-based Analysis

This approach involves collecting data using microtraffic simulation, biometric sensors (e.g., heart rate sensors), virtual reality (VR) technology, and conducting network screening based on these data. This is a potential approach in the future as technology continues to advance.

Limitations/Remaining Questions

Although the proposed framework covers various measures for conducting network screening based on different sources of data, there are several limitations/remaining technical challenges as follows:

1. Objectives/purpose of network screening: The framework does not explicitly mention the objectives or purpose of network screening. Depending on the goals, different measures and approaches may be more or less relevant. For example, if the objective is to identify high-risk locations for pedestrians or cyclists, conflict-based measures and complementary data fusion may be more appropriate than other approaches.
2. The framework should provide more information about the key questions and thresholds related to each approach. For example, what are the conflict thresholds for identifying high-risk locations? How can we determine the appropriate level of aggregation for network screening (e.g., intersection-level vs mid-block level)? Answering these questions can help to ensure that the measures are applied consistently and accurately across different locations.
3. Network elements and scope of network screening: The framework should also consider the network elements and scope of network screening. For example, the effectiveness of conflict-based measures may vary depending on the type of network element (e.g., intersections vs mid-blocks) and the location (urban vs rural, arterials vs freeways). Additionally, the feasibility of using different data sources for network screening may vary depending on the scope of the analysis (e.g., fixed point sensors for conflict analysis for mid-blocks may be less feasible than at signalized intersections).

Addressing these limitations/remaining technical challenges will require more research.

 

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