Modeling Sustainable Traffic Behavior: Avoiding Congestion at a Stationary Bottleneck

Sustainable traffic behaviour is increasing in importance as traffic volume rises due to population growth. In this paper, a model for traffic flow at a stationary bottleneck is developed to determine the parameters that cause congestion. Towards this goal, traffic density, speed, and delay were acquired during peak and off-peak periods in the morning and afternoon at a stationary bottleneck in Peshawar, KPK, Pakistan. The morning and afternoon peak periods have high densities, low speeds, and considerable delays. Regression models are developed using this data. These results indicate that there is a linear relationship between density and time at the stationary bottleneck and a negative linear relationship between density and speed. Thus, an increase in density increases the time delay and reduces the speed. I comprehensive traffic delay model is characterized by a stationary bottleneck. The Kolmogorov-Smirnov (KS) test and P-values were used to identify the best-fit distribution for speed and density. The binomial and generalized extreme values are considered the best fits for density and speed. The results presented can be used to develop accurate simulation models for stationary bottlenecks to reduce congestion. Doi: 10.28991/CEJ-2022-08-11-02 Full Text: PD

Internet-of-Video Things Based Real-Time Traffic Flow Characterization

Real-world traffic flow parameters are fundamental for devising smart mobility solutions. Though numerous solutions (intrusive and non-intrusive sensors) have been proposed, however, these have serious limitations under heterogeneous and congested traffic conditions. To overcome these limitations, a low-cost real-time Internet-of-Video-Things solution has been proposed. The sensor node (fabricated using Raspberry Pi 3B, Pi cameral and power bank) has the capability to stream 2 Mbps MJPEG video of 640x480 resolution and 20 frames per second (fps). The Camlytics traffic analysis software installed on a Dell desktop is employed for traffic flow characterization. The proposed solution was field-tested with vehicle detection rate of 85.3%. The novelty of the proposed system is that in addition to vehicle count, it has the capability to measure speed, density, time headway, time-space diagram and trajectories. Obtained results can be employed for road network planning, designing and management

Macroscopic Traffic Flow Characterization at Bottlenecks

Traffic congestion is a significant issue in urban areas. Realistic traffic flow models are crucial for understanding and mitigating congestion. Congestion occurs at bottlenecks where large changes in density occur. In this paper, a traffic flow model is proposed which characterizes traffic at the egress and ingress to bottlenecks. This model is based on driver response which includes driver reaction and traffic stimuli. Driver reaction is based on time headway and driver behavior which can be classified as sluggish, typical or aggressive. Traffic stimuli are affected by the transition width and changes in the equilibrium velocity distribution. The explicit upwind difference scheme is used to evaluate the Lighthill, Whitham, and Richards (LWR) and proposed models with a continuous injection of traffic into the system. A stability analysis of these models is given and both are evaluated over a road of length 10 km which has a bottleneck. The results obtained show that the behavior with the proposed model is more realistic than with the LWR model. This is because the LWR model cannot adequately characterize driver behavior during changes in traffic flow

Nanostructured Materials For Magnetic Refrigeration

Magnetic refrigeration based on the magnetocaloric effect (MCE) has attracted much attention because of its environmentally and operating energy cost advantages over vapor-compression refrigeration. In this regard, search for a suitable magnetic refrigerant material has been topic of intense research. Challenges faced in this regard are: (1). tailoring of transition temperature near room temperature, (2). finding a material with a large temperature change per unit of applied magnetic field, (3). materials with wide operating temperature, (4). either find materials that produce low hysteresis, or, add or make compositions to lower it, (5). easy and low-cost of fabrication of magnetocaloric regenerator (MR), (6) a minimal volume of the required (and costly) permanent magnet array, (7) corrosion resistance, nontoxicity, and high thermal conductivity. The magnetocaloric effect (MCE) arises from changes in the magnetic order of materials. The most appreciable MCE can be expected in the vicinity of magnetic phase transitions induced by temperature and/or magnetic fields. The application of a magnetic field vector causes the magnetic moments of a magnetic material tend to align parallel to it, and the thermal energy released in this process heats the material. Reversibly, the magnetic moments become randomly oriented on magnetic field removal cooling down the material. The value of the MCE depends on the difference in the magnetic state before and after temperature or field induced phase transitions (PT). The largest MCE is expected at first-order transition (FOT) changes in magnetization though hysteresis effect, which lowers the efficiency of magnetic refrigerant, is greatest with this transitions. The MCE related to a second order transition (SOT) possesses significantly less hysteresis but MCE properties associated are also not that large. Literature survey [Khattak 15] observed an enhancement of MCE properties by at least 40% when a material is fabricated as a nanostructure on account of broadening of Magnetocaloric curve, as well as reduction in its hysteresis. Different aspects such as the size, shape, chemical composition, structure and interaction of the nanostructure with the surrounding matrix and neighboring particles all have a profound effect on the magnetic behavior of a material. Moreover, after extensive literature survey [Khattak 15] it has been observed that at ambient temperature (between 260–340K) three families Gd5(Si xGe1_x)4, MnFe(P,As) and La(FexSi 1_x)13 stand out based on their MCE properties, each with their own shortcomings such as hysteresis loss, toxicity and fabrication difficulties respectively. The purpose of this dissertation is to study each of the aforementioned families not just on a single criteria, but on all variables of MCE e.g. Curie Temperature Tc, Magnetic Entropy Change Δ|SM|, Adiabatic Temperature ΔTad and Relative Cooling Power RCP with missing variables calculated wherever possible for thorough analytical and comparisons purposes. Effects of impurities, heat treatment, synthesis methods and doping of different materials on MCE properties and hysteresis on the aforementioned families would be analyzed, with the aim of identifying the best sample composition and preparation methods for the nanostructure synthesis among each family. Moreover one of basic hurdle is to find materials with wide operating temperature span. In this regard a new approach, where instead of a single material a combination of nanostructure materials in a matrix, is being proposed. Each material in the matrix has its Tc at different points on the desired temperature scale thus giving a wide range of operating temperature span. An added purpose of this dissertation is to propose a group of suitable materials used in combination in a matrix for enhanced MCE properties over a wide operating temperature

A Novel Macroscopic Traffic Model based on Distance Headway

A new model is proposed to characterize changes in traffic at transitions. These changes are affected by driver response. The distance headway between vehicles is considered as it affects driver behavior. Driver response is quick with a small distance headway and slow when the distance headway is large. The variations in traffic are greater with a slow driver while traffic is smooth with a quick driver. A model is developed which characterizes traffic based on driver response and distance headway. This model is compared with the well-known and widely employed Zhang and PW models. The Zhang model characterizes driver response at transitions using an equilibrium velocity distribution and ignores distance headway and driver response. Traffic flow in the PW model is characterized using only a velocity constant. Roe decomposition is employed to evaluate the Zhang, PW, and proposed models over a 270 m circular (ring) road. Results are presented which show that Zhang model provides unrealistic results. The corresponding behavior with the proposed model has large variations in flow with a slow driver but is smooth with a quick driver. The PW model provides smooth changes in flow according to the velocity constant, but the behavior is unrealistic because it is not based on traffic physics. Doi: 10.28991/CEJ-SP2021-07-03 Full Text: PD

Non-homogeneous traffic characterization based on driver reaction and stimuli

A macroscopic model for non-homogeneous traffic is proposed based on harmonization during transitions. This model considers the lateral and forward distances between vehicles, reaction and harmonization times, and changes in velocity. Further, the equilibrium velocity distribution is characterized based on the density and travel time of real non-homogeneous traffic. The proposed and Payne–Whitham (PW) models are evaluated over a 200 m circular road using the FORCE scheme. The results obtained demonstrate that the proposed model provides a more realistic representation of non-homogeneous traffic

Magnetocaloric properties of metallic nanostructures

A compilation of magnetocaloric properties of metallic nanostructures with Curie temperature (TC) between 260 and 340 K has been tabulated. The tabulated data show that nanostructure plays an important role in enhancing the magnetocaloric properties of a material, namely by reducing the peak of magnetic entropy, but broadening of the magnetocaloric effect curve with an average of 10 K sliding window for Curie temperature. A second table lists all bulk metallic and intermetallic materials, in which there is no nanostructural data, with an entropy change of at least 20 J/kg K and a Curie temperature between 260 and 340 K. We propose that further experiments should be made on the nanostructured form of these materials

Magnetocaloric properties of metallic nanostructures

Abstract: A compilation of magnetocaloric properties of metallic nanostructures with Curie temperature (T C ) between 260 and 340 K has been tabulated. The tabulated data show that nanostructure plays an important role in enhancing the magnetocaloric properties of a material, namely by reducing the peak of magnetic entropy, but broadening of the magnetocaloric effect curve with an average of 10 K sliding window for Curie temperature. A second table lists all bulk metallic and intermetallic materials, in which there is no nanostructural data, with an entropy change of at least 20 J/kg K and a Curie temperature between 260 and 340 K. We propose that further experiments should be made on the nanostructured form of these materials

Macroscopic traffic characterization based on driver memory and traffic stimuli

A new macroscopic traffic flow model is proposed which incorporates traffic alignment behavior at transitions. In this model, velocity is a function of the distance headway and driver response time. It can be used to characterize the traffic flow for both uniform and non uniform headways. The well-known Zhang model characterizes this flow based on driver memory which can produce unrealistic results. The performance of the proposed Khan-Imran-Gulliver (KIG) and Zhang models is evaluated for an inactive bottleneck on a 2000 m circular road. The results obtained show that the traffic behavior with the KIG model is more realistic