When is interference with traffic flow allowed

Du, L. Guo Ed. IGI Global. Available In. DOI: Current Special Offers. No Current Special Offers. As a paradigm of decentralized advanced traveler information systems ATIS , VANETs have obtained interests of researchers in both communication and transportation fields. Abstract This chapter presents the gap acceptance theory, in that it is of great applicative interest in studying road intersections.

Therefore, some models for estimating the critical gap and the follow-up time for at-grade unsignalized intersections and roundabouts are described. Please log in to get access to this content Log in Register for free. To get access to this content you need the following product:.

Springer Professional "Technik" Online-Abonnement. Springer Professional "Wirtschaft" Online-Abonnement. Hevelius edizioni In Italian, available by info hevelius. Further, take in account the four subcases below: i Case 2. As for the connectivity probability , it can be expressed by. Using the same derivation method as , the probability that road segments and are connected is given by where. Similarly, the probability that segments and are connected is given by where. Thus, the connectivity probability of the entire road segment in Case 2.

Therefore, the connectivity probability of the entire road segment can be expressed by. Regarding the four subcases above, the connectivity probability in Case 2.

Only if one of the two following subcases occurs, it may be possible that the entire road segment is connected. Regarding the two subcases above, the connectivity probability in Case 2. Regarding all the analysis mentioned above in Case 2 , the connectivity probability of the entire road segment in Case 2 is expressed by. Case 3. In Case 3 , as shown in Figure 5 , the entire road segment can be divided into three subsegments , , and , denoting , , and , respectively.

The following probabilities are easy to obtain:. According to whether there are vehicles driving on segment , Case 3 can be divided into two subcases as follows: i Case 3. In Case 3. Further, take into account the four subcases below: i Case 3.

As for connectivity probabilities and , they can be expressed by and respectively. Using the same derivation method as , the probability that the segments and are connected can be given by where And the probability that the segments and are connected can be given by where.

Thus, the connectivity probability of the entire road segment in Case 3. Regarding the four subcases above, the connectivity probability in Case 3. Only if one of the two following subcases occurs may it be possible that the entire road segment is connected: i Case 3. Regarding the two subcases above, the connectivity probability in Case 3.

Regarding all the analysis mentioned above in Case 3 , the connectivity probability of the entire road segment in Case 3 is expressed by. Case 4. In Case 4 , as shown in Figure 6 , the entire road segment can be divided into three subsegments , , and , denoting , , and , respectively. According to whether there are vehicles driving on segment , Case 4 can be divided into two subcases as follows: i Case 4. In Case 4. Further, take into account the four subcases below: i Case 4. Using the same derivation method as , the probability that the segments and are connected can be driven by where.

And the probability that the segments and are connected can be driven by where. Thus, the connectivity probability of the entire road segment in Case 4. Only if one of the two following subcases occurs may it be possible that the entire road segment is connected: i Case 4. Regarding all the analysis mentioned above in Case 4 , the connectivity probability of the entire road segment in Case 4 is expressed by. Therefore, regarding all the analysis mentioned above, the connectivity probability of the entire road segment can be expressed by.

In this section, we verify the accuracy and effectiveness of our proposed analytical model through simulation experiments. In addition, we analyze and discuss the impact of different key parameters on connectivity performance, including vehicle arrival rate , vehicle communication range , the length of entire road , vehicle normal speed , and safe speed. For each simulation result, it is an average of trials. Figure 7 shows the impact of accident location on connectivity probability.

From the curve in traffic accident condition, we can see that the connectivity probability increases first and then decreases with different accident location, which shows that the vehicles in the middle part of the road have a greater influence on the connectivity. When the traffic accident occurs at location 0, the traffic flow in can be same as that in normal condition.

Hence, the connectivity probability with accident location 0 is equal to that with normal condition. Note that the traffic accident, which makes the vehicles slow down rather than block the traffic flow, can improve the connectivity to a certain degree. Figures 8 — 11 all show the impact of vehicle arrival rate on connectivity probability. Obviously, the connectivity probability increases with the increase of vehicle arrival rate.

It is because that the vehicle density can increase with the increase of arrival rate, which improves the road connectivity. What is more, from Figures 8 — 11 , we can see that the deviation between analytical result and simulation result decreases with the increase of vehicle arrival rate. The reason is that the vehicles have a relatively large arrival randomness and inter-vehicle spacing with a small arrival rate.

In addition, Figure 8 shows the impact of vehicle communication range on connectivity probability. The connectivity probability increases with the increase of vehicle communication range, which is because that the two vehicles can obtain a greater opportunity to be connected with a larger communication range.

Note that the connectivity probability has a larger improvement with the communication range from m to m, comparing with other communication range gaps. It can offer guidance to the design of signal transmitting power. Figure 9 shows the impact of the length of entire road on connectivity probability. It can be seen that the connectivity probability decreases with the increase of the length of road. The reason is that, with constant probability that two adjacent vehicles are connected, the larger the length of road is, the larger the number of vehicles is, the smaller the connectivity probability of all vehicles is.

Consequently, the connectivity probability decreases. Figure 10 shows the impact of vehicle normal speed on connectivity probability. It can be seen that the connectivity probability decreases with the increase of vehicle normal speed. The reason is that the vehicle density in normal segment decreases with the increase of normal speed.

Moreover, the normal segment occupies a relatively large proportion of the entire road. Therefore, the connectivity probability of the entire road decreases relatively obviously. Figure 11 shows the impact of vehicle safe speed on connectivity probability. It can be seen that the connectivity probability decreases slightly with the increase of vehicle safe speed. The reason is the similar to the impact of normal speed on connectivity probability.

However, the deceleration segment occupies a small proportion of the entire road, resulting in a slow decline in probability. In this paper, we analyzed the connectivity in vehicular networks with slight traffic interference, i. An analytical model was proposed to calculate the connectivity probability of the entire road segment. The road segment can be in a highway scenario or a sparse urban scenario, which means that the slight traffic interference can only make the vehicles slow down, rather than block the traffic flow.

When a traffic accident occurs, it is necessary to inform the vehicles within communication range which will pass by the accident location to slow down. Once passing by the accident location, their speeds can return to the normal value. Moreover, on the basis of the analytical model, we analyzed the impact of several key parameters on connectivity probability, including vehicle arrival rate, vehicle communication range, the length of road, vehicle normal speed, and safe speed.

All analytical results are verified through simulation experiments. In the future, we will consider the acceleration and deceleration process in a more practical scenario, which is much more complicated. Note that we assume the vehicle arrival follows the Poisson process; namely, intervehicle spacing follows the exponential distribution. How about the network connectivity with other intervehicle spacing distribution? In addition, in order to facilitate the analysis, the length of deceleration segment is set to the value of vehicle communication range.

The different cases with different values of the length of deceleration segment are also our future work. The data used to support the findings of this study are available from the corresponding author upon request. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors. Read the winning articles. Journal overview. Special Issues. Academic Editor: Petros Nicopolitidis. Received 28 Aug Accepted 25 Nov