ConTraEmSis: The Congested Traffic and Emission Index Impact Analysis Amid the Large-Scale Social Restrictions (LSSR) of COVID-19 in Several Cities of West Java, Indonesia

In some metropolitan cities of West Java Province, the urban movability affects to the congested traffic. This study analyzes the congested traffic during the large-scale social re-strictions (LSSR) of the coronavirus disease 2019 (COVID-19) around some metropolitan cities of West Java Province in May-June 2020. The national government of the Republic of Indonesia declared a national’s LSSR just for some the essential and critical activities are being acquiesced still enforce. Our proposed method, namely, ConTraEmSis is contributed to analyze the congested traffic two months from May-June 2020. We exploit the geomatic of the congested traffic in the COVID-19 information & coordination center West Java province (PIKOBAR) dataset for Bogor, Depok, and Bekasi (Bodebek areas), Bandung areas, and whole West Java province areas. We exploit the road transport and traffic management center (RTTM) and area traffic control system (ATCS) and index their data for the Bodebek and Bandung areas. The ConTraEmSis demonstrates that the congested traffic the LSSR of COVID-19 reduces around 18%-49% every month. Instead, the Java government always launch the many good governance policies to support the LSSR of the COVID-19 system. proposed model shows that after the LSSR in 2019 and 2020, is reduced between 3.27% and 5.27%. On the week-day afternoon, we scale down between 4.52% and 4.74%. On the weekend morning, we cut down between 1.3% to 1.5% before and after LSSR of 2019 and 2020, respectively. The congested traffic trends get 24% and 41% in the weekdays and weekends, respectively, since the LSSRs. Onto the weekend afternoon, we perform 13.4% and 14.8% for reducing the CO2 emission index during the LSSR’s 2020. We achieve the important congestion get an emission index lower than 0.3.

The Decline of User Experience in Transition from Automated Driving to Manual Driving

Automated driving technologies are rapidly being developed. However, until vehicles are fully automated, the control of the dynamic driving task will be shifted between the driver and automated driving system. This paper aims to explore how transitions from automated driving to manual driving affect user experience and how that experience correlates to take-over performance. In the study 20 participants experienced using an automated driving system during rush-hour traffic in the San Francisco Bay Area, CA, USA. The automated driving system was available in congested traffic situations and when active, the participants could engage in non-driving related activities. The participants were interviewed afterwards regarding their experience of the transitions. The findings show that most of the participants experienced the transition from automated driving to manual driving as negative. Their user experience seems to be shaped by several reasons that differ in temporality and are derived from different phases during the transition process. The results regarding correlation between participants’ experience and take-over performance are inconclusive, but some trends were identified. The study highlights the need for new design solutions that do not only improve drivers’ take-over performance, but also enhance user experience during take-over requests from automated to manual driving.

Merging and crossing movements in the congested and the non-congested traffic flow

The congested traffic flow is significantly different from both the free flow and the non-congested but limited flow. Two of those differences are in the merging and crossing movements. Based on the conducted measurements, this movement could be up to 10 times faster in the congested condition. Another important feature is the giveway gestures. It was found that the vast majority of the gaps needed to merge or cross are not generated by the traffic dynamic, but created by the arterial road's drivers in the interactions with drivers wishing to merge or cross.

Smart Alarm Traffic Management System for the Major Road to Isolation Hospital during the Covid-19 Pandemic in Port Said City

Transportation is an important feature that affects the quality of life. Huge increase in population, modernization in all aspects of life, and cities expansion lead to a more congested traffic that may be acceptable for in-emerging trips but enormous for emergency trips, especially for COVID 19 patients with severe respiratory symptoms. Smart transportation techniques offer solutions to the congestion problemsfor different modes of transportation and traffic management. In this paper, a smart traffic solution to the congestion problem in the major road to isolation hospital in Port Said City is presented.

Assessment of Discretionary Lane-Changing Decisions using a Random Parameters Approach with Heterogeneity in Means and Variances

Lane-changing maneuvers on highways may cause capacity drops, create shock waves, and potentially increase collision risks. Properly managing lane-changing behavior to reduce these adverse impacts requires an understanding of their determinants. This paper investigates the determinants of lane changing in congested traffic using a next generation simulation dataset. A random parameters binary logit model with heterogeneity in means and variances was estimated to account for unobserved heterogeneity in lane-changing behavior across vehicles. Estimation results show that average headway, the original lane of the vehicle, driver acceleration/deceleration behavior, and vehicle size all significantly influence lane-changing probabilities. It was further found that the effect of vehicle size varied significantly across observations, that the mean of this variation decreased with increasing average headway, and the variance increased with increasing driver acceleration/deceleration. These empirical findings provide interesting new evidence on the determinants of lane changing, which can be used in traffic flow models to better replicate and predict traffic flow.

An Improved CTM Model for Urban Signalized Intersections and Exploration of Traffic Evolution

In this paper is going to be proposed a Cell Transmission Model (CTM), its analysis and evaluation with a case study, which addresses in a detailed way the aspect of merging and diverging operations on urban arterials. All those few CTM models that have been developed so far, to model intersections, have some limitations and drawbacks. First, unlike the simple composition road networks, such as highways, urban arterials must include some complex parts called merge sand diverges, due to the fact of vibrational values of reduced capacity, reduced saturation flow rate, etc. In order to simulate an urban network/arterial it is not possible to neglect the traffic signal indication on the respective time step. The objective of this paper is to highlight the difference between the results of the original CTM and our proposed CTM and to provide evidence that the later one is better than the old one. The proposed and formulated model will be employed through an algorithm of CTM to model a segment- arterial road of Pristina (compound from signalized intersections). For the functionalization and testing of the proposed model is build the experimental setup that is compatible with the algorithm created on C# environment. Results show that the proposed model can describe light and congested traffic condition. In light traffic conditions, in great mass traffic flow is dictated by the traffic signal status, while in medium congestion is obtained a rapid increase of the density to each cell. Fluctuations of the density from the lowest to the highest values are obvious during the first three cycles to all cells of the artery in a congested traffic state. Doi: 10.28991/cej-2021-03091659 Full Text: PDF

Intelligence in traffic simulation model: Modeling congested network

Traffic simulation tools are becoming more popular as complexity and intelligence are growing in transportation systems. The need for more accurate and intelligent traffic modeling is increasing rapidly as transportation systems are having more congestion problems. Although traffic simulation models have been continuously updated to represent various traffic conditions more realistically, most simulation models still have limitations in overcapacity congested traffic conditions. In traditional traffic simulation models, when there is no available space due to traffic congestion, additional traffic demand may never be allowed to enter the network. The objective of this paper is to investigate one possible method to address the issue of unserved vehicles in overcapacity congested traffic conditions using the VISSIM trip chain. The VISSIM trip chain is used for this analysis as it has the advantage of holding a vehicle without eliminating it when traffic congestion prevents its entrance onto a network. This will allow the vehicle to enter when an acceptable gap becomes available on the entry link. To demonstrate the difference between the simulation using standard traffic input and the trip chain method, a sample congested traffic network is built and congested traffic scenarios are created. Also, simulations with different minimum space headway parameters in the priority rules are analyzed to model congested traffic conditions more realistically. This will provide the insight about the sensitivity of the model to this parameter. Based on the analysis conducted it is concluded that, with appropriate calibrations, the trip chain feature in VISSIM has the potentials to be useful in modeling overcapacity congested traffic conditions more realistically.