Human Factors of XR: Using Human Factors to Design XR Systems
Using Vehicular Networks to Collect Common Traffic Data
1. Using Vehicular Networks to Collect Common
Traffic Data
Hadi Arbabi and Michele C. Weigle
Department of Computer Science, Old Dominion University
Figure 2. Message reception rate from VS2 in 5 km segment with 50%
penetration rate and medium traffic flow.
Figure 3. Message delay from VS2 with different delivery methods.
Figure 1. Two TOs and four dynamically defined VS in a highway.
Table 1 shows the ability of DTMon to provide good quality
estimation of time mean speed (TMS), travel time, and
Methods of Message Delivery in DTMon space mean speed (SMS) compared to current technologies
Regular Forwarding (RF) – A vehicle passing a VS will such as fixed point sensors and detectors (e.g., ILDs) and
forward the message (including time, speed, location) to the probe vehicle-based systems (e.g., AVL).
closest possible TO from the list of TOs defined in the task. Table 1. Overall comparison of DTMon with other technologies.
Dynamic Transmission Range (DTR) – A vehicle will use (t-test alpha=0.05)
RF initially with the standard DSRC range of 300 m. If the Sensors and
Good Estimate? AVL DTMon
message cannot be forwarded (i.e., there is no vehicle Detectors
within 300 m), then the vehicle will increase its transmission Flow Rate and
Yes No See Table 2
range to 600 m. If the vehicle is still not able to find a Density
neighbor, it will increase its transmission range to 1000 m. TMS Yes Underestimate Yes
Store-and-Carry (SAC) – A vehicle will store the message Travel Time Not Available Overestimate Yes
and carry it to the next TO. SMS Not Available Underestimate Yes
RF+SAC – A vehicle will forward the message to the Vehicle Classification Not Accurate Limited Yes
closest TO using RF and will store and carry the message
to the next TO in order to ensure reception. Table 2 shows the recommended methods of message
DTR+SAC - A vehicle will forward the message to the delivery in DTMon considering conditions such as distance
closest TO using DTR and store and carry the message to from the TO, traffic density, and market penetration rate.
the next TO.
Table 2. Required method, traffic density, or penetration rate for high
Quality Estimation of Traffic Data quality estimation of traffic data with 95% confidence (t-test alpha=0.05)
We have evaluated DTMon using ns-3 in free-flow and Traffic Density
transient traffic with different market penetration rates. High Quality Message
or
Estimation Delivery
Figure 2 shows the percentage of received messages from Penetration
Conf. ≥ 95% Method
Rate
vehicles passing VS2 (1 km from TO1 and 3 km from TO5)
using different message delivery methods in medium traffic Flow Rate and
High Any
flow rate (1800 vehicles/h). Density
Figure 3 shows the average delay for messages received by SAC, RF
Classification,
the TOs from VS2. RF and DTR have delays in milliseconds. +SAC,
TMS Low
or
Delay using SAC varies by the travel time of the segment. Travel Time,
DTR+SAC
More forwarding takes place using RF+SAC and DTR+SAC, or
SMS Medium or
which results in a lower average delay than SAC. Any
High
Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu http://oducs-networking.blogspot.com/
Department of Computer Science, Old Dominion University, Norfolk, VA
ACM VANET, Beijing, China, September 2009