Distribution characteristics of sulfonamide antibiotics between water and extracellular polymeric substances in municipal sludge.

The interaction between extracellular polymeric substances (EPS) in municipal sludge and antibiotics in wastewater is critical in wastewater treatment, resource recovery, and sludge management. Therefore, it is increasingly urgent to investigate the distribution coefficient (Log K) of sulfonamide antibiotics (SAs) in EPS, particularly in sludge-derived dissolved organic carbon (DOC) and aqueous phase systems. Herein, through balance experiments, the concentrations of SAs were determined using alkaline extraction EPS (AEPS) and alginate-like extracellular polymer (ALE) systems, and the Log KDOC values were determined. The results showed that the Log KDOC of AEPS was higher than that of ALE, which exhibited a negative KDOC value, indicating an inhibitory effect on dissolution. For the three SAs studied, the Log KDOC values were in the following order: sulfamethoxazole > sulfapyridine > sulfadiazine. This order can be attributed to the differing physicochemical properties, such as polarity, of the SAs. Three-dimensional excitation-emission matrix fluorescence spectra and fitting results indicated a lack of aromatic proteins dominated by tryptophan and humus-like substances in ALE. Meanwhile, the hydrophobic interaction of aromatic proteins dominated by tryptophan was the main driving force in the binding process between AEPS and SAs.

A Ti3 C2 TX /Pt-Pd based amperometric biosensor for sensitive cancer biomarker detection.

The detection of cancer biomarkers is of great significance for the early screening of cancer. Detecting the content of sarcosine in blood or urine has been considered to provide a basis for the diagnosis of prostate cancer. However, it still lacks simple, high-precision and wide-ranging sarcosine detection methods. In this work, a Ti3 C2 TX /Pt-Pd nanocomposite with high stability and excellent electrochemical performance has been synthesized by a facile one-step alcohol reduction and then used on a glassy carbon electrode (GCE) with sarcosine oxidase (SOx ) to form a sarcosine biosensor (GCE/Ti3 C2 TX /Pt-Pd/SOx ). The prominent electrocatalytic activity and biocompatibility of Ti3 C2 TX /Pt-Pd enable the SOx to be highly active and sensitive to sarcosine. Under the optimized conditions, the prepared biosensor has a wide linear detection range to sarcosine from 1 to 1000 µM with a low limit of detection of 0.16 µM (S/N = 3) and a sensitivity of 84.1 µA/mM cm2 . Besides, the reliable response in serum samples shows its potential in the early diagnosis of prostate cancer. More importantly, the successful construction and application of the amperometric biosensor based on Ti3 C2 TX /Pt-Pd will provide a meaningful reference for detecting other cancer biomarkers.

Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis.

Connected vehicle (CV) technologies are changing the form of traditional traffic models. In the CV driving environment, abundant and accurate information is available to vehicles, promoting the development of control strategies and models. Under these circumstances, this paper proposes a bidirectional vehicles information structure (BDVIS) by making use of the acceleration information of one preceding vehicle and one following vehicle to improve the car-following models. Then, we deduced the derived multiple vehicles information structure (DMVIS), including historical movement information of multiple vehicles, without the acceleration information. Next, the paper embeds the four kinds of basic car-following models into the framework to investigate the stability condition of two structures under the small perturbation of traffic flow and explored traffic response properties with different proportions of forward-looking or backward-looking terms. Under the open boundary condition, simulations on a single lane are conducted to validate the theoretical analysis. The results indicated that BDVIS and the DMVIS perform better than the original car-following model in improving the traffic flow stability, but that they have their own advantages for differently positioned vehicles in the platoon. Moreover, increasing the proportions of the preceding and following vehicles presents a benefit to stability, but if traffic is stable, an increase in any of the parameters would extend the influence time, which reveals that neither ß1 or ß2 is the biggest the best for the traffic.

Traffic Vehicle Counting in Jam Flow Conditions Using Low-Cost and Energy-Efficient Wireless Magnetic Sensors.

The jam flow condition is one of the main traffic states in traffic flow theory and the most difficult state for sectional traffic information acquisition. Since traffic information acquisition is the basis for the application of an intelligent transportation system, research on traffic vehicle counting methods for the jam flow conditions has been worthwhile. A low-cost and energy-efficient type of multi-function wireless traffic magnetic sensor was designed and developed. Several advantages of the traffic magnetic sensor are that it is suitable for large-scale deployment and time-sustainable detection for traffic information acquisition. Based on the traffic magnetic sensor, a basic vehicle detection algorithm (DWVDA) with less computational complexity was introduced for vehicle counting in low traffic volume conditions. To improve the detection performance in jam flow conditions with a "tailgating effect" between front vehicles and rear vehicles, an improved vehicle detection algorithm (SA-DWVDA) was proposed and applied in field traffic environments. By deploying traffic magnetic sensor nodes in field traffic scenarios, two field experiments were conducted to test and verify the DWVDA and the SA-DWVDA algorithms. The experimental results have shown that both DWVDA and the SA-DWVDA algorithms yield a satisfactory performance in low traffic volume conditions (scenario I) and both of their mean absolute percent errors are less than 1% in this scenario. However, for jam flow conditions with heavy traffic volumes (scenario II), the SA-DWVDA was proven to achieve better results, and the mean absolute percent error of the SA-DWVDA is 2.54% with corresponding results of the DWVDA 7.07%. The results conclude that the proposed SA-DWVDA can implement efficient and accurate vehicle detection in jam flow conditions and can be employed in field traffic environments.

Sensor Location Problem Optimization for Traffic Network with Different Spatial Distributions of Traffic Information.

To obtain adequate traffic information, the density of traffic sensors should be sufficiently high to cover the entire transportation network. However, deploying sensors densely over the entire network may not be realistic for practical applications due to the budgetary constraints of traffic management agencies. This paper describes several possible spatial distributions of traffic information credibility and proposes corresponding different sensor information credibility functions to describe these spatial distribution properties. A maximum benefit model and its simplified model are proposed to solve the traffic sensor location problem. The relationships between the benefit and the number of sensors are formulated with different sensor information credibility functions. Next, expanding models and algorithms in analytic results are performed. For each case, the maximum benefit, the optimal number and spacing of sensors are obtained and the analytic formulations of the optimal sensor locations are derived as well. Finally, a numerical example is proposed to verify the validity and availability of the proposed models for solving a network sensor location problem. The results show that the optimal number of sensors of segments with different model parameters in an entire freeway network can be calculated. Besides, it can also be concluded that the optimal sensor spacing is independent of end restrictions but dependent on the values of model parameters that represent the physical conditions of sensors and roads.

Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO).

The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the Markov blanket (MB) algorithm is employed to extract the main factors associated with hazardous traffic events; (2) a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle's speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G) have significant influences on hazardous traffic events. The sequential minimal optimization (SMO) algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles.

A novel abandoned object detection system based on three-dimensional image information.

A new idea of an abandoned object detection system for road traffic surveillance systems based on three-dimensional image information is proposed in this paper to prevent traffic accidents. A novel Binocular Information Reconstruction and Recognition (BIRR) algorithm is presented to implement the new idea. As initial detection, suspected abandoned objects are detected by the proposed static foreground region segmentation algorithm based on surveillance video from a monocular camera. After detection of suspected abandoned objects, three-dimensional (3D) information of the suspected abandoned object is reconstructed by the proposed theory about 3D object information reconstruction with images from a binocular camera. To determine whether the detected object is hazardous to normal road traffic, road plane equation and height of suspected-abandoned object are calculated based on the three-dimensional information. Experimental results show that this system implements fast detection of abandoned objects and this abandoned object system can be used for road traffic monitoring and public area surveillance.

A novel multisensor traffic state assessment system based on incomplete data.

A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based on the probe vehicle detector and fixed detector, a fusion model of space matching is presented to estimate the mean travel speed of the road. Finally, the traffic flow data include flow, speed and, occupancy rate, which are detected between Beijing Deshengmen bridge and Drum Tower bridge, are fused to assess the traffic state of the road by using the fusion decision model of rough sets and cloud. The accuracy of experiment result can reach more than 98%, and the result is in accordance with the actual road traffic state. This system is effective to assess traffic state, and it is suitable for the urban intelligent transportation system.

High sensitivity and automatic chemiluminescence detection of glucose and lactate using a spin-disc paper-based device.

This paper reports a spin-disc paper-based device with 10 individual detection units containing electromagnetic modules controlling the sample incubation time before chemiluminescence (CL) signal detection. After the sample was added to the top paper chip and incubated with the enzyme, the electromagnet was turned off to allow contact between the top and bottom paper. The H2O2 generated by the sample flowed vertically to the bottom paper and initiated the oxidase of the luminol to generate the CL signal. After one detection the disc was automatically rotated to the next position to repeat the above detection. The advantage of using the device over the lateral flow and the in situ detection was firstly proved using the detection of H2O2 and the glucose/lactate sample with 5 minute incubation. The CL intensity was increased 300 times/1000 times as the glucose/lactate was incubated for 5 minutes compared to the non-incubated samples. Afterward, the device was employed to separately detect glucose and lactate diluted in PBS, artificial sweat, artificial saliva, and fresh cell culture media. Finally, the device was employed to detect the glucose and lactate in the media collected over the 24 hour culture of PC3 cells. The uptake and production rates of glucose and lactate were correspondingly determined as 0.328 ± 0.015 pmol h-1 per cell and 1.254 ± 0.053 pmol h-1 per cell, respectively. The reported device has wide application potential due to its capabilities in automatic detection of multiple samples with very high sensitivity and small sample volume (down to 0.5 µL).

Integrated driving risk surrogate model and car-following behavior for freeway risk assessment.

Drivers' risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible for enhancing traffic safety. In this study, a novel approach for evaluating driving interaction risk on freeways is presented. The approach involves the integration of an interaction risk perception model with car-following behavior. The proposed model, named the driving risk surrogate (DRS), is based on the potential field theory and incorporates a virtual energy attribute that considers vehicle size and velocity. Risk factors are quantified through sub-models, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. The DRS model is applied to assess driving risk in a typical scenario on freeways, and car-following behavior. A sensitivity analysis is conducted on the effect of different parameters in the DRS on the stability of traffic dynamics in car-following behavior. This behavior is then calibrated using a naturalistic driving dataset, and then car-following predictions are made. It was found that the DRS-simulated car-following behavior has a more accurate trajectory prediction and velocity estimation than other car-following methods. The accuracy of the DRS risk assessments was verified by comparing its performance to that of traditional risk models, including TTC, DRAC, MTTC, and DRPFM, and the results show that the DRS model can more accurately estimate risk levels in free-flow and congested traffic states. Thus the proposed risk assessment model provides a better approach for describing vehicle interactions and behavior in the digital world for both researchers and practitioners.

A freeway vehicle early warning method based on risk map: Enhancing traffic safety through global perspective characterization of driving risk.

In the era of rapid advancements in intelligent transportation, utilizing vehicle operating data to evaluate the risk of freeway vehicles and study on vehicle early warning methods not only lays a theoretical foundation for improving the active safety of vehicles, but also provides the technical support for reducing accident rate. This paper proposes a freeway vehicle early warning method based on risk map to enhance vehicle safety. Firstly, Modified Time-to-Collision (MTTC), a two-dimensional indicator that describes the risk of inter-vehicle travel, is used as an indicator of road traffic risk. This paper designs a transformation function to probabilistically transform MTTC into Risk Indicators (RI). The single-vehicle risk map is generated based on the mapping relationship between the risk values and the corresponding roadway segments. These single-vehicle risk maps of all vehicles on the road are superimposed to construct the risk map, which is used to describe the risk distribution in the freeway. Then, a vehicle early warning framework is built based on the risk map. The risk values in the risk map are compared with predefined early warning thresholds to alert the vehicle when it enters a high-risk state. Finally, VISSIM is used to carry out simulation experiments. The experiment simulates a freeway accident stopping situation. This scenario includes sub-scenarios such as unplanned stopping and lane-changing, continuous lane-changing, and adjacent lane overtaking. We analyze the risk map and vehicle warning results in different sub-scenarios, evaluate the risk changes of the vehicles before and after receiving the warning, and compare the warning results of the method in this paper with other alternative methods. The method is applied to 17 vehicles in the simulation to adjust their motion states. The results show that the total warning time is reduced by 29.6% and 73.3% of vehicles change lanes away from the accident vehicle. The overall results validate the effectiveness of the vehicle early warning method based on risk map proposed in this paper.

Enhancing predictions of remedial reagent transport via a vertical groundwater circulation well with high-resolution aquifer characterization.

The vertical groundwater circulation well (GCW) is a commonly used technique in contaminated sites to remove secondary contaminants from low permeable zones. Early GCW studies often used simple subsurface hydraulic properties, such as anisotropic homogeneous aquifers or low conductivity lens/blocks, to mimic the complex subsurface heterogeneity. Although studies based on simplified representations of aquifer heterogeneity provide straightforward flow and transport information for engineering design of a GCW, they may over- or under-estimate contaminant fate and transport in the field. The objective of this study is to identify key heterogeneity factors that control the capture zone extension and to examine the extent to which the accuracy of estimated heterogeneity spatial distributions influences the prediction of remedial reagent transport. To achieve these objectives, we utilized Monte Carlo simulation to investigate the extension of the circulation zone in heterogeneous aquifers and to identify the key factors that contribute most to the variability of the circulation zone. Three commonly used geostatistical approaches (equivalent homogeneous, kriging, and highly parameterized methods) were employed to estimate the spatial distributions of key factors. The reliabilities of these estimated fields were evaluated through their remedial reagent transport predictability. The key factor analysis revealed that the mean porosity value, the variance of lnK, and the correlation length of lnK profoundly influence the lateral expansion of the capture zone. Neglecting the aquifer hydraulic conductivity heterogeneity underestimates the extension of the circulation zone and the spread of remedial reagent. Additionally, utilizing a highly parameterized approach to estimate the high-resolution K field can accurately reproduce the key remedial reagent distributions. The concentration arrival time and peak concentration are significantly improved compared to those predictions based on the equivalent homogeneous and kriged K fields.

Continuous production of bimetallic nanoparticles on carbon nanotubes based on 3D-printed microfluidics.

Nanoparticle-functionalized carbon nanotubes are promising in many research fields, especially in sensing, due to their intriguing performance in catalysis. However, these nanomaterials are mainly produced through batch processes under harsh conditions, thus encountering inherent limitations of low throughput and uncontrollable morphology of functional nanoparticles (NPs). In this work, we propose a method for high-yield and continuous production of bimetallic (Pt-Pd) NPs on multi-walled carbon nanotubes (MWCNTs) at room temperature through a custom 3D-printed microfluidic platform. A homogenous particle nucleation and growth environment could be created on the microfluidic platform that was equipped with two 3D-printed micromixers. Pt-Pd NPs loaded on MWCNTs were prepared in the microfluidic platform with high throughput and controlled size, dispersity and composition. The synthetic parameters for these nanocomposites were investigated to optimize their electrocatalytic performance. The optimized nanocomposites exhibited excellent electrocatalytic activity with exceptional sensitivity and wide detection range, superior to their counterparts prepared via conventional approaches. This method proposed here could be further adapted for manufacturing other catalyst support materials, opening more avenues for future large-scale production and catalytic investigation of functional nanomaterials.

Occurrence and mechanism of sulfamethoxazole in alginate-like extracellular polymers from excess sludge.

The recovery of biopolymers, particularly alginate-like extracellular polymers, from municipal sludge represents a promising step toward sustainable sludge treatment practices. Originating from wastewater plants in complexly polluted environments, alginate-like extracellular polymers carry potential environmental risks concerning their reuse. This study employs ultrahigh-performance liquid chromatography-tandem mass spectrometry to investigate the distribution coefficients and occurrence of alginate-like extracellular polymers and sulfamethoxazole. Results demonstrate a negative distribution coefficient, suggesting an inhibitory effect on sulfamethoxazole dissolution. The ethanol-extracted alginate-like extracellular polymers exhibits higher sulfamethoxazole levels (approximately 52%) than those obtained via dialysis extraction. Three-dimensional excitation-emission matrix analysis and adsorption studies indicate the absence of tyrosine-like substances in the alginate-like extracellular polymers, unlike in other extracellular polymeric substances. This absence diminishes hydrophobic interactions, highlighting that electrostatic interactions play a more important role. These insights are crucial for understanding the adsorption behavior of alginate-like extracellular polymers and optimizing their large-scale extraction processes.

Development of a novel microfluidic biosensing platform integrating micropillar array electrode and acoustic microstreaming techniques.

Quantifying biomarkers at the early stage of the disease is challenging due to the low abundance of biomarkers in the sample and the lack of sensitive techniques. This article reports the development of a novel microfluidic electrochemical biosensing platform to address this challenge. The electrochemical sensing is achieved by utilizing a micropillar array electrode (µAE) coated with 3D bimetallic Pt-Pd nanotrees to enhance the sensitivity. A bubble-based acoustic microstreaming technique is integrated with the device to increase the contact of analyte molecules with the surface of electrodes to further enhance the electrochemical performance. The current density of Pt-Pd NTs/µAE with acoustic microstreaming is nearly 22 times that of the bare planar electrode in potassium ferrocyanide solution. The developed biosensor has demonstrated excellent sensing performance. For hydrogen peroxide detection, both the Pt-Pd NTs/µAE and acoustic microstreaming contribute to the sensitivity enhancement. The current density of the Pt-Pd NTs/µAE is approximatively 28 times that of the bare µAE. With acoustic microstreaming, this enhancement is further increased by nearly 1.6 times. The platform has a linear detection range of 5-1000 µM with a LOD of 1.8 µM toward hydrogen peroxide detection, while for sarcosine detection, the linear range is between 5 and 100 µM and LOD is 2.2 µM, respectively. Furthermore, the sarcosine biosensing shows a high sensitivity of 667 µA mM-1∙cm-2. Such a sensing platform has the potential as a portable device for high sensitivity detection of biomarkers.

Multiplexed detection of biomarkers using a microfluidic chip integrated with mass-producible micropillar array electrodes.

Quantifying multiple biomarkers with high sensitivity in tiny biological samples is essential to meet the growing demand for point-of-care testing. This paper reports the development of a novel microfluidic device integrated with mass-producible micropillar array electrodes (µAEs) for multiple biomarker detections. The µAE are mass-fabricated by soft lithography and hot embossing technique. Pt-Pd bimetallic nanoclusters (BNC) are modified on the surface of µAEs by constant potential (CP)/multi-potential step (MPS) electrodeposition strategies to improve the electroanalytical performance. The experimental result displays that Pt-Pd BNC/µAEs have good sensitivity enhancement compared with bare planar electrodes and bare µAEs, the enhancement being 56.5 and 9.5 times respectively, from the results of the H2O2 detection. Furthermore, glucose, uric acid and sarcosine were used as model biomarkers to show the biosensing capability with high sensitivity. The linear range and LOD of the glucose, uric acid and sarcosine detection are 0.1 mM-12 mM, 10 µM-800 µM and 2.5 µM-100 µM, 58.5, 3.4 and 0.4 µM, respectively. In particular, biosensing chips show wide linear ranges covering required detection ranges of glucose, uric acid and sarcosine in human serum, indicating the developed device has great potential in self-health management and clinical requirements.

[N2O Emission from the Processes of Wastewater Treatment: Mechanisms and Control Strategies].

As a direct carbon emission source, the amount of nitrous oxide (N2, which is actually caused by AOB denitrification. To control the N2O emission during biological N-removal, complete HND and NO2- accumulation for AOB denitrification should be avoided to a large extent. For this purpose, DO in aerobic tanks should be controlled at a normal level (approximately 2 mg·L-1), and solid retention time (SRT) should be extended, up to 20 d, which would avoid accumulating N2O for AOB denitrification. Additionally, external carbon should be supplemented in time to promote HDN approaching the end, N2. This review summarizes the mechanisms of all the mentioned N2O emission pathways and discusses the control strategies of N2O emission according to the associated mechanisms.

Key Factors Analysis of Severity of Automobile to Two-Wheeler Traffic Accidents Based on Bayesian Network.

The purpose of this paper is to analyze the complex coupling relationships among accident factors contributing to the automobile and two-wheeler traffic accidents by establishing the Bayesian network (BN) model of the severity of traffic accidents, so as to minimize the negative impact of automobile to two-wheeler traffic accidents. According to the attribution of primary responsibility, traffic accidents were divided to two categories: the automobile and two-wheeler traffic as the primary responsible party. Two BN accident severity analysis models for different primary responsible parties were proposed by innovatively combining the Kendall correlation analysis method with the BN model. A database of 1560 accidents involving an automobile and two-wheeler in Guilin, Guangxi province, were applied to calibrate the model parameters and validate the effectiveness of the models. The result shows that the BN models could reflect the real relationships among the influential factors of the two types of traffic accidents. For traffic accidents of automobiles and two-wheelers as the primary responsible party, respectively, the biggest influential factors leading to fatality were weather and visibility, and the corresponding fluctuations in the probability of occurrence were 32.20% and 27.23%, respectively. Moreover, based on multi-factor cross-over analysis, the most influential factors leading to fatality were: {Off-Peak Period → Driver of Two-Wheeler: The elderly → Driving Behavior of Two-Wheeler: Parking} and {Drunk Driving Two-Wheeler → Having a License of Automobiles → Visibility: 50 m~100 m}, respectively. The results provide a theoretical basis for reducing the severity of automobile to two-wheeler traffic accidents.

Level of Service Model of the Non-Motorized Vehicle Crossing the Signalized Intersection Based on Riders' Perception Data.

This article aims to analyze the factors affecting the LOS (level of service) of non-motorized vehicles crossing the signalized intersection and to construct an appropriate method to evaluate the LOS. Aiming at the mixed non-motorized traffic flow of electric vehicles and bicycles in the Chinese metropolis, the delay model in the highway capacity manual (HCM) was modified by taking two new factors into account: the pedestrian traffic rule compliance rate and the fuzzy perception of arrival rate in reality. The results show that the data obtained by the modified model are more consistent with the actual one. Next, a comparison was established between the linear regression method and cumulative logistic regression to determine the variables that affect the LOS, and finally, a LOS evaluation index system based on threshold schemes was defined. The recommended LOS model can provide corresponding references for traffic engineers who seek to improve the level of service in urban intersections.

Microscopic Simulating the Impact of Cruising for Parking on Traffic Efficiency and Emission with Parking-and-Visit Test Data.

Cruising for parking creates a moving queue of cars that are waiting for vacated parking spaces, but no one can see how many cruisers are in the queue because they are mixed with normal cars. In order to mitigate the influence of cruising for parking on normal cars, the simulation framework based on VISSIM was proposed for reproducing the cruising vehicles and normal traffic flows. The car-following model of cruising vehicles was calibrated by the GPS and video data. The scenarios under different cruising ratios were analyzed to evaluate the influence of cruising for parking on traffic efficiency and emissions. Finally, the layout optimization changing the parking locations and positions of entrance-exit gates were discussed to mitigate the negative effect. The results indicated that cruising for parking deteriorates the traffic congestion and emissions on the road sections, intersections and network. The closer distances the intersections and sections are to the parking lot, the greater the negative impact is. But the negative effect after the 30% proportion on traffic performance only illustrates the slight deterioration, because the carrying capacity of the network is reached. The research results provide a quantitative method for the hidden contribution of cruising for parking on traffic congestion and emissions.