Low Penetration Rate Research Articles

Extended Observer for Urban Traffic Control Based on Limited Measurements From Connected Vehicles

Current Urban Traffic Control (UTC) systems rely heavily on inductive loop detectors. The emergence of Connected Vehicles (CV) opens new possibilities for improving signal control, while reducing the need for loop detectors. However, for low penetration rates, the CV measurements are sporadic and thus difficult to exploit by existing UTC systems. In this paper, a methodology that enables cycle-to-cycle traffic state estimation and prediction based on limited CV measurements is presented. Furthermore, the proposed formulation enables fusion of CV with other data sources and their integration in any UTC system. The developed Extended Observer (EO) is a discrete-time, variable-dimension implementation of the Extended Kalman Filter. It does not require loop detectors and is independent of the type of signal control. The evaluation focuses on the queue length estimation. Especially in oversaturation, the EO outperforms the CV measurements for all examined penetration rates. Moreover, the highest benefit is observed for the lowest penetration rates. The results show that for oversaturation and low penetration rates the EO improves the CV measurements 13-31%. In addition, the EO is tested with an adaptive UTC system by feeding the fused estimation from CV and camera measurements. The error in queue length estimation from the EO is significantly lower than the error based on stochastic arrivals. Additionally, the results show a reduction in delays at the examined signal and the complete intersection. Overall, this paper sheds light on the potential benefits from enhancing limited CV measurements in order to contribute immediately to current UTC systems.

Fostering Insurance Awareness among the Masses in Bangladesh: Challenges and Opportunities

The economic growth of Bangladesh is now remarkably higher compared to any other time of the country. This has been observed primarily in last few decades in Bangladesh. According to World Bank Report, the Gross National Income (GNI) tremendously increased from $100 to $1,220 between 1972 to 2015. Now, Bangladesh is trying to achieve its goal to be a middle-income country by 2021 by reaching steady growth of 8% per year (Bhattacharya, & Khan, 2018). Since Bangladesh is a country with full of natural calamities and risks in economic development, insurance industry has got very high potential in this country. It is the most potentials place for insurance business in the world due to its vulnerable economy and God gifted disasters. In the report, Lloyd's identified the under insurance or insurance gap, which depicts the value of assets at risk not covered fully by insurance policies in Bangladesh. Among the 43 countries covered in the report, Bangladesh has the largest insurance gap, which stands at $5.50 billion, or 2.10 percent of the country's gross domestic product (GDP). Insurance premium per capita in Bangladesh is just USD $8, a statistic that masks the fact that most people do not have insurance at all (Vaughan, & Vaughan, 2007). Research also shows that (Lloyd’s Report 2019) showed that 1% rise in penetration in insurance sector of Bangladesh can contribute to as much as $4.4bn in savings in the country. In comparison to the trend of per capita income $1602 as of 2017, the insurance penetration rate in Bangladesh is 0.70% in global comparison during 2016 which is very low compared to other countries of the world. Out of which only 0.2% consists of life insurance in Bangladesh. This very low penetration rate in insurance sector is very alarming for a country having 170 million people. On the other hand, the highest insurance penetration in the world is at 18%, belonging to Taiwan. According to the World Bank statistics, only 4 out of 1,000 people in Bangladesh have life insurance policies which is one of the lowest in the world. Hence, there is a very high potentials of insurance business in Bangladesh.

Globally applicable water quality simulation model for river basin chemical risk assessment

Abstract Many companies around the world need to assess water environmental risks in accordance with sustainable development goals (SDGs) set by the United Nations, environmental, social, and governance (ESG) investment, the Carbon Disclosure Project (CDP) water program, and also their corporate social responsibility (CSR) activities. Although companies have been requested to voluntarily evaluate their water risks and provide information thereon, the water quality risk in river basins is difficult to assess owing to insufficient monitoring information. This paper proposes the National Institute of Advanced Industrial Science and Technology - Standardized Hydrology-based AssessmeNt tool for chemical Exposure Load (AIST-SHANEL) model as a river water quality simulation model that can be employed for chemical risk assessment and evaluation of the effects of use of a company's consumer products on river basins. AIST-SHANEL is the only unsteady analysis model that estimates spatial and temporal chemical concentrations and assesses water quality within river basins by point/non-point emission of chemical substances. To evaluate the efficacy of AIST-SHANEL, a case study focused on the surfactant linear alkylbenzene sulfonate (LAS), found in the detergents of widely used health care products, was conducted. The water risk assessment was performed in three Japanese river basins with different watershed characteristics: Tama River in urban Tokyo with high sewerage penetration rate, Nikko River in urban Nagoya with a low sewerage penetration rate, and the nonurban None River with no sewerage. In Tama River, the LAS river water concentrations were found to be strongly influenced by seasonal variations as well as the flow rate associated with dilution and temperature-induced biodegradation. Their concentrations in Nikko River were strongly affected by flooding, indicating that the influence on their transport through the drainage channel was due more to low sewage levels than temperature-induced biodegradation. In None River, the LAS river water concentrations were only slightly affected by dilution. The river sediment concentrations of LAS in the three river basins showed mild seasonal variations. The 95th percentile of river water concentrations, predicted environmental concentration (PEC), at the most downstream and maximum concentration grids in the three river basins were 4.5 and 51.2 mg/m3 for Tama River, 30 and 58.6 mg/m3 for Nikko River, and 0.15 mg/m3 for None River. Neither of the PECs nor maximum values at these grids exceeded the predicted no-effect concentration (PNEC) of LAS (270 mg/m3), suggesting minimal ecological risk in these river basins over the year for all target river basins due to LAS. Furthermore, we analyzed a scenario in which LAS was substituted with the highly biodegradable alcohol ethoxylates (AE) for corporate self-management measures in Nikko River with only a few sewers, and therefore only a low risk. The AIST-SHANEL model has useful and widespread application with no need for monitoring by companies; as long as the relevant watershed characteristics can be obtained, water risk assessments of chemical substances can be performed for any river basin in the world.

Sampled Trajectory Data-Driven Method of Cycle-Based Volume Estimation for Signalized Intersections by Hybridizing Shockwave Theory and Probability Distribution

The cycle-based volume is critical for traffic state estimation and signal control optimization at signalized intersections. Traditional volume estimation mainly depends on fixed detectors represented by loop detectors, but limited spatial coverage and detection failure are also prominent. With the development of vehicle positioning, smartphone-based navigation, and connected-vehicle technologies, massive high-resolution trajectory data have recently become available, which can provide rich and timely information on the traffic arrival and departure processes at signalized intersections. Hence, the studies utilizing trajectory data for estimating the queue length and traffic volume at intersections has received increasing attention in the past few years. However, the most existing studies have demanded a comparatively high penetration rate and adopted site-specific assumptions for unsteady arrival patterns. In contrast, this paper solely used trajectory data for cycle-based flow estimation through a generic hybrid method that combined a probabilistic model and shockwave theory to maximize the utilization of limited captured trajectories, especially under a low penetration rate. In this method, within each cycle, the volume of stopped vehicles is estimated based on the shockwave theory, while the volume of non-stopped vehicles is modeled as a parameter estimation problem of a time-dependent constrained Poisson distribution, where the time headway correspondingly obeys an M3 distribution. The cycle-based volume is solved by a maximum likelihood estimation using an expectation-maximization procedure. An empirical case study was conducted with various signal timing schemes and the results showed satisfactory robustness with an accuracy of more than 90% under a penetration rate of 7.6%.

Dynamic Research and Experimental Analysis of a New Downhole Drilling Tool

With the increasing of drilling depth, the working conditions of the drilling engineering are becoming more complicated. When drilling in the complex well structure, there are many new problems or challenges such as low rate of penetration (ROP), serious stick–slip phenomenon, or high risk of downhole tools failure. The most critical factor in solving these new challenges is improving rock breaking efficiency. Considering this, this paper proposed a new oscillator based on the positive displacement motor. Combining with new complex drilling conditions, the new structure was designed. The mathematical models were presented, including the working mechanism of the new design with its structure parameters. Furthermore, the theoretical models of dynamics under new conditions were analyzed, including the new bottom hole assembly. Based on this, we can get the vibration displacement, velocity, and the system vibration spectrum at different drill string positions. Compared with the results of a numerical example, the lab test was carried out. The two important parameters of vibration frequency and impact forces were compared with the theoretical results, which verified the correctness of the theoretical model and the accuracy of the solution method. Moreover, field experiments were also tested to verify the results, and the field experimental data showed that the new design could increase ROP obviously. The method and conclusions of the research can provide a new reference for the new downhole technology of enhancing the ROP or improving the rock breaking efficiency in complex well structure.

Cycle-Based End of Queue Estimation at Signalized Intersections Using Low-Penetration-Rate Vehicle Trajectories

Queue length is a crucial measure of intersection performance. Probe vehicles (PVs) with advanced sensors are capable of recording vehicle trajectories that can be used to estimate queue length, a technique of which has received considerable attention in the past decade. Noticeably, this technique usually requires high PV penetration rates (e.g., above 25%) in order to ensure estimation accuracy. Though the PVs are expected to increase, their penetration rate will still remain relatively low in the near future. Meanwhile, the initial queue length is another important factor that directly relates to queue dynamics at each cycle. However, most of the studies failed to adequately account for the effect of the initial queue on cyclic queue length estimation. To address the above challenges, this paper proposes a cycle-based end of queue estimation method using sampled vehicle trajectory data under relatively low penetration rates. Two major steps are involved: first, vehicle arrival process is modeled as a certain distribution in line with traffic conditions and an expectation maximum (EM) procedure is employed to estimate the arrival rate of each cycle; then, both ends of the queue and initial queue are estimated at each cycle based on shockwave theory. Microscopic traffic simulator VISSIM is utilized to examine the performance of the method. The experimental results reveal that the cycle-based end of the queue can be estimated with desirable accuracy in different scenarios, e.g., undersaturated, oversaturated, and queue spillback conditions. The comparison with the state-of-the-art methods further helps to verify the advantage of the method, especially under low-penetration-rate conditions.

KEPUTUSAN PEMBELIAN ASURANSI JIWA PADA KONSUMEN RURAL: PENGARUH BAURAN PEMASARAN JASA

Indonesia is one of the most promising markets for the growth of life insurance business in Southeast Asia. Low penetration rate and huge population that is scattered in many islands emerge as opportunity and challenge for any company that is willing to operate its insurance business in this country. This study aims to examine the effects of services marketing mix namely product, price, place, promotion, people, process and physical evidence on life insurance purchase decision of rural customers in Kutai Kartanegara Regency, East Kalimantan Province. Data collected through self-administered survey to 76 life insurance policyholders in 3 sub-districts in Kutai Kartanegara Regency such as Tenggarong, Kota Bangun and Sebulu that represented cluster-rural area of East Kalimantan. Multiple linear regression model was used to analyze the data and the results revealed that product, price, people, and physical evidence positively and significantly affect life insurance purchase decision, while place shown negative and significant effect. On the other hand, we found that promotion and process do not significantly affect purchase decision on life insurance.

Effect of Traffic Light Scenario on VANETs Connectivity with Low Penetration Rate

Traffic light with Vehicular Ad-Hoc Networks (VANETs) has been developed recently. Many issues and challenges come up with this development that required an investigation. One of the most prominent challenges is connectivity under low penetration of vehicles with VANET technology. Traffic light scenario has been proposed in this field to overcome low penetration problem, but no studies have been done on their connectivity. In this paper, VANET connectivity with traffic light scenarios had been investigated. The most significant metric in this investigation is penetration rate, more specifically low values of it. Connectivity measurements, i.e. reachability, connection duration and re-healing time had been studied with real-time simulation. Three traffic light scenarios had been considered; no traffic light, normal traffic light and intelligent traffic light. The effect of these scenarios on VANET connectivity had been analyzed with low penetration rate and important conclusions had been stated for future works.

New Study of Mgo Nps in Drilling Fluid to Reduce Stick-Slip Vibration in Drilling System

Stick-slip is kind of vibration which associated with drilling operation in around the bottom hole assembly (BHA) due to the small clearance between drill string & the open hole and due to the eccentric rotating of string. This research presents results of specific experimental study that was run by using two types of drilling mud (Fresh water Bentonite & Polymer), with/without Nanoparticle size materials of MgO in various ratios and computes the rheological properties of mud for each concentration [Yield point, plastic viscosity, Av, PH, filter loss (30 min), filter cake, Mud Cake Friction, Friction Factor]. These results then were used to find a clear effects of Nanoparticle drilling mud rheology on stick - slip strength by several perspectives through a special “Torque and Drag” software which simulate the torque amount expected on BHA during drilling a vertical well in different conditions using real drilling string design that usually used in Iraqi oil fields. Thus to mitigate or to prevent stick–slip and cure the sequence events that could happen to both of drilling string and the well, i.e. Bit/BHA wear, pipe sticking, borehole instability and low Rate of penetration. Our study concluded that there are good reduction in the torque from (2031lb-ft) to (1823lb-ft) using polymer mud and torque reduction from (4000lb-ft) to (3450lb-ft) using Fresh Water Bentonite, these results do not include any breaking in the satisfactory range of other mud rheology.

The scalp localization system of neurosurgery based on augmented reality theory

Neurosurgery navigation system, which is expensive and complicated to operate, has a low penetration rate, and is only found in some large medical institutions. In order to meet the needs of other small and medium-sized medical institutions for neurosurgical navigation systems, the scalp localization system of neurosurgery based on augmented reality (AR) theory was developed. AR technology is used to fuse virtual world images with real images. The system integrates computed tomography (CT) or magnetic resonance imaging (MRI) with the patient's head in real life to achieve the scalp positioning. This article focuses on the key points of Digital Imaging and Communications in Medicine (DICOM) standard, three-dimensional (3D) reconstruction, and AR image layer fusion in medical image visualization. This research shows that the system is suitable for a variety of mobile phones, can achieve two-dimensional (2D) image display, 3D rendering and clinical scalp positioning application, which has a certain significance for the auxiliary neurosurgical head surface positioning.

An analysis of the factors affecting mobile commerce adoption in developing countries

PurposeThis study aims to investigate the factors that may hinder or facilitate consumers’ adoption of mobile-commerce (m-commerce) activities in the context of developing countries exemplified here by Oman.Design/methodology/approachA conceptual model was developed through integrating factors from UTAUT2 (performance expectancy, expectancy effort, social influence, facilitating conditions, hedonic motivation, price value, habit and self-efficacy) and SERVQUAL (system quality, service quality and information quality). Data were collected from 530 Omani m-commerce users through a cross-sectional survey.FindingsThe results of the structural equation modelling showed that consumers’ behavioural intention (BI) towards m-commerce adoption was significantly influenced by information quality, habit, performance expectancy, trust, hedonic motivation, service quality, price value and facilitating conditions, in their order of influencing strength, and explained 65.5 per cent of the variance in BI. Unexpectedly, effort expectancy, social influence, self-efficacy and system quality had no significant effect on BI.Practical implicationsThis study will explain the currently relatively low penetration rate of m-commerce adoption in Oman, which will help local m-commerce businesses to develop the right organizational strategies, especially related to marketing strategies and developing mobile applications, which will draw the attention of many users.Originality/valueThis is one of the few studies that integrates UTAUT2 with SERVQUAL and tests the proposed model in non-Western cultural contexts. Specifically, in contrast to previous studies, diversity of individuals’ acceptance behaviour is examined in Oman.

Effect of the surface charge of entrapped polypropylene at nanosilica-composite on cuttings transport capacity of water-based muds

Field experience has shown that inadequate cuttings transportation and hole cleaning results in wellbore drilling challenges, such as pipe sticking, high torque and drag, low rate of penetration, formation damage, etc. Designing a drilling mud with optimum rheological properties would be crucial to improve cuttings transportation efficiency. Several studies have shown that drilling muds that combine polymers with silica nanoparticles (SiO2 NPs) to form polymer nanocomposite (PNC) can effectively modify the rheology and enhance the filtration properties of water-based muds (WBMs). Studies on the significant decrease of the yield point that may lead to a huge reduction in the transport capacity of drilled cuttings using polypropylene-silica nanocomposite (PSNC) have not been reported. In this study, the effect of surface charge of newly developed PSNC on rheological and filtration characteristics of WBMs were examined. The results from zeta potential measurements showed that the newly developed PSNC and bentonite particles are both negatively charged at different levels of pH, which would lead to decrease in the yield point of the PSNC bearing mud samples. This problem was resolved using (3-aminopropyl) triethoxysilane (APTES) to modify the surface of the newly developed PSNC. From the experimental results, the amino-modified PSNC showed better performance in controlling filtration and modifying rheology than the unmodified PSNC due to the electrostatic attraction between bentonite and modified PSNC. The concentration of 1.5 g of the modified PSNC has a significantly higher yield point of 16.0 lb/100 ft2 than the unmodified PSNC with 6.4 lb/100 ft2 at 78 °F; demonstrating its capacity to provide better cuttings transportation than the unmodified PSNC. The surface functionalization of the newly developed PSNC in WBMs showed promising results in the rheology and filtration properties which demonstrates the viability of using them in drilling operations.

Glycyrrhizin-Assisted Transport of Praziquantel Anthelmintic Drug through the Lipid Membrane: An Experiment and MD Simulation.

Praziquantel (PZQ) is one of the most widespread anthelmintic drugs. However, the frequent insufficient application of PZQ after oral administration is associated with its low solubility, penetration rate, and bioavailability. In the present study, the permeation of PZQ through a 1,2-dioleoyl- sn-glycero-3-phosphocholine (DOPC) membrane was investigated to probe glycyrrhizin-assisted transport. Glycyrrhizin (or glycyrrhizic acid, GA), a natural saponin, shows the ability to enhance the therapeutic activity of various drugs when it is used as a drug delivery system. However, the molecular mechanism of this effect is still under debate. In the present study, the transport rate was measured experimentally by a parallel artificial membrane permeation assay (PAMPA) and molecular dynamics (MD) simulation with DOPC lipid bilayers. The formation of the noncovalent supramolecular complex of PZQ with disodium salt of GA (Na2GA) in an aqueous solution was proved by the NMR relaxation technique. PAMPA experiments show a strong increase in the amount of the penetrating praziquantel molecules in comparison with a saturated aqueous solution of pure drug used as a control. MD simulation of PZQ penetration through the bilayer demonstrates an increase in permeability into the membrane in the presence of a glycyrrhizin molecule. A decrease in the free energy barrier in the middle of the lipid bilayer was obtained, associated with the hydrogen bond between PZQ and GA. Also, GA reduces the local bilayer surface resistance to penetration of PZQ by rearranging the surface lipid headgroups. This study clarifies the mechanism of increasing the drug's bioavailability in the presence of glycyrrhizin.

Quality of Experience-Aware Mobile Edge Caching through a Vehicular Cloud

Densification through small cells and caching in base stations have been proposed to deal with the increasing demand for Internet content and the related overload on the cellular infrastructure. However, these solutions are expensive to install and maintain. Instead, using vehicles acting as mobile caches might represent an interesting alternative. In our work, we assume that users can query nearby vehicles for some time, and be redirected to the cellular infrastructure when the deadline expires. Beyond reducing costs, in such an architecture, through vehicle mobility, a user sees a much larger variety of locally accessible content within only few minutes. Unlike most of the related works on delay tolerant access, we consider the impact on the user experience by assigning different retrieval deadlines per content. In our paper, we provide the following contributions: (i) we model analytically such a scenario; (ii) we formulate an optimization problem to maximize the traffic offloaded while ensuring user experience guarantees; (iii) we propose two variable deadline policies; (iv) we perform realistic trace-based simulations, and we show that, even with low technology penetration rate, more than 60 percent of the total traffic can be offloaded which is around 20 percent larger compared to existing allocation policies.

Hash-based space partitioning approach to iris biometric data indexing

In spite of high efficiency of the iris recognition systems, the accuracy of the system degrades with the increase in the size of database, hence there is need for indexing. One of the important characteristics of such an indexing approach is to have high tolerance against feature deviation due to noise. In this work, an indexing approach has been proposed which deals with the feature deviation due to variation in quality of iris images. Further, an efficient index space has been developed using two set of hash functions. During identification, a set of probable bin location for the queried data is decided depending upon the level of noise in the query image. Such a mechanism will help in carrying out efficient searching of the queried data. Next, the list of candidate data set which are very similar to the query data are retrieved from the probable bin locations. Considering retrieval of iris template up to the fifth rank, the proposed approach gives high hit rate even at low penetration rate when tested with, IITD, CASIA-V3 Interval and UBIRIS 2 iris databases compared to the existing approaches.

Trajectory-Based Measurement of Signalized Intersection Capacity

Capacities of road intersections are a limiting factor and crucial for the performance of road networks. Therefore, for purposes of intersection design and of optimal signal timing, numerous methodologies have been proposed to either estimate or directly measure the capacity of single movements at road intersections. However, both model-based estimation and direct measurement suffer from the large effort that is needed to gather the relevant data. Even worse, once the data are collected they only represent a snapshot of the capacity over time. This paper proposes an alternative approach to estimate capacity of signalized road intersections over time using only automatically generated trajectories of probe vehicles. The obtained capacity can be used to evaluate the effective degree of saturation using real demand, or to assess hypothetic different conditions in demand or signaling. The cyclic operation of signalized intersections allows for the accumulation of trajectories, and thus in practical applications for the compensation of potentially low penetration rates. Within a sequential process the intersection’s cycle time and the approach green time and saturation flow rates are determined. The determination of the cycle time and the green times is based on an existing approach. The derivation of the saturation flow rates relies on its direct dependency to the saturation time headway and uses two parameters to be calibrated. Testing with a commercial dataset on an intersection in Munich produced a good signal timing estimation and saturation flow values that are comparable to a calculation based on the German guideline.

Improving the Stability of Metal Halide Perovskite Quantum Dots by Encapsulation.

Metal halide perovskite quantum dots (PQDs), with excellent optical properties and spectacular characteristics of direct and tunable bandgaps, strong light-absorption coefficients, high defect tolerance, and low nonradiative recombination rates, are highly attractive for modern optoelectronic devices. However, the stability issue of PQDs remains a critical challenge of this newly emerged material despite the recent rapid progress. Here, the encapsulation strategies to improve the stability of PQDs are comprehensively reviewed. A special emphasis is put on the effects of encapsulation, ranging from the improvement of chemical stability, to the inhibition of light-induced decomposition, to the enhancement of thermal stability. Particular attention is devoted to summarizing the encapsulation approaches, including the sol-gel method, the template method, physical blending, and microencapsulation. The selection principles of encapsulation materials, including the rigid lattice or porous structure of inorganic compounds, the low penetration rate of oxygen or water, as well as the swelling-deswelling process of polymers, are addressed systematically. Special interest is put on the applications of the encapsulated PQDs with improved stability in white light-emitting diodes, lasers, and biological applications. Finally, the main challenges in encapsulating PQDs and further investigation directions are discussed for future research to promote the development of stable metal halide perovskite materials.

Integrated Traffic Control for Freeways using Variable Speed Limits and Lane Change Control Actions

The wide deployment of vehicle automation and communication systems (VACS) in the next decade is expected to influence traffic performance on freeways. Apart from safety and comfort, one of the goals is the alleviation of traffic congestion which is a major and challenging problem for modern societies. The paper investigates the combined use of two feedback control strategies utilizing VACS at different penetration rates, aiming to maximize throughput at bottleneck locations. The first control strategy employs mainstream traffic flow control using appropriate variable speed limits as an actuator. The second control strategy delivers appropriate lane-changing actions to selected connected vehicles using a feedback-feedforward control law. Investigations of the proposed integrated scheme have been conducted using a microscopic simulation model for a hypothetical freeway featuring a lane-drop bottleneck. The results demonstrate significant improvements even for low penetration rates of connected vehicles.

Traffic parameters estimation for signalized intersections based on combined shockwave analysis and Bayesian Network

This paper proposes a framework to combine shockwave analysis (SA) with Bayesian Network (BN) for traffic flow parameters estimation at signalized intersections using vehicle trajectory data. Traffic SA is applied as the basis to derive the analytical expression of individual vehicle trajectory given the fundamental diagram (FD) parameters (e.g., capacity, jam density, free flow speed) as well as the traffic volume. According to SA, the analytical probability distribution of each vehicle’s travel time (defined as the time of a vehicle travelling between an upstream and a downstream location at a signalized intersection) is derived. This probability distribution is parameterized by traffic volume and FD parameters. We then apply a three-layer BN model to construct the relationship between traffic volume, FD parameters, sampled vehicles’ arrival times and intersection travel times. Since traffic volume cannot be measured directly from sampled vehicle trajectories, an expectation maximization (EM) algorithm is introduced to perform learning for estimating the cycle-by-cycle volume (or arrivals) and FD parameters. Such estimated parameters are then used, via SA, to estimate vehicles’ intersection travel times. The proposed combined SA-BN model is evaluated using microscopic traffic simulation data and the NGSIM dataset. Sensitivity analysis of the estimation performance with respect to penetration rates is conducted. In general, the proposed method yields promising estimation accuracy, with the mean absolute percentage error (MAPE) of the estimation bounded by 15%, generally around 5%, as compared with the true value, even under low penetration rates.

Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections.

With the development of intelligent transportation system (ITS) and vehicle to X (V2X), the connected vehicle is capable of sensing a great deal of useful traffic information, such as queue length at intersections. Aiming to solve the problem of existing models’ complexity and information redundancy, this paper proposes a queue length sensing model based on V2X technology, which consists of two sub-models based on shockwave sensing and back propagation (BP) neural network sensing. First, the model obtains state information of the connected vehicles and analyzes the formation process of the queue, and then it calculates the velocity of the shockwave to predict the queue length of the subsequent unconnected vehicles. Then, the neural network is trained with historical connected vehicle data, and a sub-model based on the BP neural network is established to predict the real-time queue length. Finally, the final queue length at the intersection is determined by combining the sub-models by variable weight. Simulation results show that the sensing accuracy of the combined model is proportional to the penetration rate of connected vehicles, and sensing of queue length can be achieved even in low penetration rate environments. In mixed traffic environments of connected vehicles and unconnected vehicles, the queuing length sensing model proposed in this paper has higher performance than the probability distribution (PD) model when the penetration rate is low, and it has an almost equivalent performance with higher penetration rate while the penetration rate is not needed. The proposed sensing model is more applicable for mixed traffic scenarios with much looser conditions.