Longitudinal Speed Profiles for Digital Map Energy Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing navigation systems lack information on the most efficient speeds and acceleration-deceleration rates for specific road segments, leading to inefficient energy use and inaccurate real-time traffic flow assessments.
Innovation Solution
The method involves collecting probe data during free flow traffic conditions to derive longitudinally distributed speeds, which are then compared to a vehicle's instantaneous speed to provide real-time energy-efficient driving instructions and assess traffic flow efficiency, using a digital map augmented with Raw Road Design Speed Limit (RRDSL) and Optimum Longitudinal Speed Profile (OLSP) data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional routing methods use maximum speed limits to calculate travel time estimates, then the calculation is simple, but the speed information is not accurate because these speeds are not always possible at various times of the day
Solution Approach 1:
The system performs preliminary processing of probe data to create speed profiles for different time slots before they are needed for routing calculations. These pre-computed speed profiles are stored and can be quickly retrieved during navigation, avoiding the need for intensive real-time processing while providing accurate time-dependent speed information.
Solution Approach 2:
The system processes a representative subset of probe data from multiple vehicles to derive average speed profiles for road segments. Instead of processing all possible data or using complex real-time analytics, it uses sufficient sample data to create accurate speed profiles that capture typical driving conditions at different times of day.
2Measurement precision
If speed profiles are derived by intensively processing probe data to create average traffic speeds for each road segment, then the speed information becomes more accurate, but the processing complexity and computational resources increase
Solution Approach 1:
Speed profiles are pre-computed during off-peak times using historical probe data, dividing the day into time slots and calculating average speeds for each road segment. These pre-computed profiles are stored in the navigation device, allowing fast retrieval during routing without performing intensive processing in real-time.
Solution Approach 2:
The day is segmented into multiple time slots (e.g., morning rush hour, midday, evening rush hour), and speed profiles are created separately for each time slot. This segmentation allows the system to capture time-dependent traffic patterns while keeping each individual profile computation manageable and efficient.
3Reliability
If navigation systems provide detailed speed profile information for routing calculations, then the routing accuracy improves, but the data storage requirements and system complexity increase
Solution Approach 1:
The system stores speed profile data at the level of road segments rather than entire routes or networks. Each road segment has its own speed profile for different time slots, allowing the system to provide detailed local speed information where needed while keeping overall data storage manageable by only storing segment-level characteristics.
Data Source
AI summary
Probe data collected at times of low traffic density is analyzed to derive a Raw Road Design Speed Limit (RRDSL, 16) for each road segment or group of segments in a digital map. The RRDSL (16), comprised of longitudinally distributed speeds, is associated with the road segment and stored in a digital medium to indicate the limits of the road section in free flow traffic. The longitudinally distributed speeds may be limited by local speed limits or other business logic to establish a Legal Raw Road Design Speed Limit (LRRDSL, 17). Either the RRDSL (16) or the LRRDSL (17) can be further modified to smooth acceleration and deceleration rates between changes in the longitudinally distributed speeds to create an Optimal Longitudinal Speed Profile (OLSP, 18), which represents optimized energy consumption. A signal can be produced if a driver's current speed rises unacceptably above a longitudinally distributed speed in real time. The signal can be audible, visible and/or haptic. Real-time traffic density information can be inferred by comparing current speed data to the longitudinally distributed speed for that position. If the current speed is consistently lower than the longitudinally distributed speed for that position, an inference is drawn that the road section is inefficient. Road efficiency assessments can be transmitted to a service center and/or other vehicles, and used by navigation software.