Identity Of Vehicle Research Articles

An Algorithm for Estimating Origins and Destinations of Shared E-Scooter Trips from Public Data-Feeds

We present an algorithm that estimates shared e-scooter trip origins and destinations using public General Bikeshare Feed Specification (GBFS) data. The model addresses challenges like GPS lag and randomized vehicle IDs. The algorithm is field-tested and validated using data for the city of Fairfax, VA. When applied to Washington, D.C., a larger city, the algorithm demonstrates scalability for larger, complex urban settings. Our methodology equips urban planners and activity center managers with a robust tool for analyzing micromobility patterns, enhancing first- and last-mile connectivity, and optimizing transportation systems—without relying on proprietary data from operators.

Weakly Supervised Contrastive Learning for Unsupervised Vehicle Reidentification.

Reidentification (Re-id) of vehicles in a multicamera system is an essential process for traffic control automation. Previously, there have been efforts to reidentify vehicles based on shots of images with identity (id) labels, where the model training relies on the quality and quantity of the labels. However, labeling vehicle ids is a labor-intensive procedure. Instead of relying on expensive labels, we propose to exploit camera and tracklet ids that are automatically obtainable during a Re-id dataset construction. In this article, we present weakly supervised contrastive learning (WSCL) and domain adaptation (DA) techniques using camera and tracklet ids for unsupervised vehicle Re-id. We define each camera id as a subdomain and tracklet id as a label of a vehicle within each subdomain, i.e., weak label in the Re-id scenario. Within each subdomain, contrastive learning using tracklet ids is applied to learn a representation of vehicles. Then, DA is performed to match vehicle ids across the subdomains. We demonstrate the effectiveness of our method for unsupervised vehicle Re-id using various benchmarks. Experimental results show that the proposed method outperforms the recent state-of-the-art unsupervised Re-id methods. The source code is publicly available on https://github.com/andreYoo/WSCL_VeReid.

Robust cryptographic scheme for reliable data communication in VANET (RCSRC) using clustering approach

In contemporary times, as accident rates continue to rise, there is a growing interest in vehicular ad hoc networks (VANETs) as a means to reduce accidents and vehicle damage, as well as enhance public safety. VANETs facilitate the transmission of messages within predefined areas to ensure system safety and efficiency. However, ensuring message authenticity in such a dynamic environment presents a significant challenge, with previous research focusing less on security aspects. Some previous protocols send plain message, which leads to the compromise of the message confidentiality. Although few algorithms work on security, they involve the communication to the trusted authority, which lead to increased latency. Thus, in this paper we propose a robust cryptographic scheme for reliable data communication in VANET which minimizes the involvement of trusted authority and uses communication ID of vehicle for communication in place of real entities. Security analysis of the proposed scheme is showing the sturdiness of the proposed protocol again various types of safety spasms. The evaluation of its performance includes assessing communication cost and time, revealing the proposed algorithm's efficiency compared to others.

Suggestions and Comparisons of Two Algorithms for the Simplification of Bluetooth Sensor Data in Traffic Cordons.

Bluetooth sensors in intelligent transportation systems possess extensive coverage and access to a large number of identity (ID) data, but they cannot distinguish between vehicles and persons. This study aims to classify and differentiate raw data collected from Bluetooth sensors positioned between various origin-destination (i-j) points into vehicles and persons and to determine their distribution ratios. To reduce data noise, two different filtering algorithms are proposed. The first algorithm employs time series simplification based on Simple Moving Average (SMA) and threshold models, which are tools of statistical analysis. The second algorithm is rule-based, using speed data of Bluetooth devices derived from sensor data to provide a simplification algorithm. The study area was the Historic Peninsula Traffic Cord Region of Istanbul, utilizing data from 39 sensors in the region. As a result of time-based filtering, the ratio of person ID addresses for Bluetooth devices participating in circulation in the region was found to be 65.57% (397,799 person IDs), while the ratio of vehicle ID addresses was 34.43% (208,941 vehicle IDs). In contrast, the rule-based algorithm based on speed data found that the ratio of vehicle ID addresses was 35.82% (389,392 vehicle IDs), while the ratio of person ID addresses was 64.17% (217,348 person IDs). The Jaccard similarity coefficient was utilized to identify similarities in the data obtained from the applied filtering approaches, yielding a coefficient (J) of 0.628. The identity addresses of the vehicles common throughout the two date sets which are obtained represent the sampling size for traffic measurements.

A pharmachain IoT with internal attack classification framework using PBFT-MI-RIB-RBF technique in healthcare

The pharmaceutical supply chain, which ensures that drugs are accessible to patients in a trusted process, is a complex arrangement in the healthcare industry. For that, a secure pharmachain framework is proposed. Primarily, the users register their details. Then, the details are converted into cipher text and stored in the blockchain. If a user requests an order, the manufacturer receives the request, and the order is handed to the distributor. Labeling is performed through Hypergeometric Distribution Centroid Selection K-Medoids Clustering (HDCS-KMC) to track the drugs. The healthcare Pharmachain architecture uses IoT to control the supply chain and provide safe medication tracking. The framework includes security with a classifier and block mining consensus method, boosts performance with a decision controller, and protects user and medication information with encryption mechanisms. After that, the drugs are assigned to vehicles, where the vehicle ID and Internet of Things (IoT) sensor data are collected and pre-processed. Afterward, the pre-processed data is analyzed in the fog node by utilizing a decision controller. Now, the status ID is generated based on vehicle id and location. The generated status ID is meant for fragmentation, encryption, and block mining processes. If a user requests to view the drug’s status ID, then the user needs to get authentication. The user’s forking behavior and request activities were extracted and given to the classifier present in the block-mining consensus algorithm for authentication purposes. Block mining happens after authentication, thereby providing the status ID. Furthermore, the framework demonstrates an efficaciousness in identifying assaults with a low False Positive Rate (FPR) of 0.022483% and a low False Negative Rate (FNR) of 1.996008%. Additionally, compared to traditional methods, the suggested strategy exhibits good precision (97.869%), recall (97.0039%), accuracy (98%), and F-measure (97.999%).

A blockchain-based data storage architecture for Internet of Vehicles: Delay-aware consensus and data query algorithms

The recent development of the Internet of Vehicles (IoV) relies heavily on the secure store and analysis of reliable driving data. Blockchain technology has been widely used in the IoV field due to its security advantages, where consensus and query algorithms are two key modules determining its performance. However, existing solutions do not perform well enough in terms of delay performance, thus limiting their ability to support IoV environments with ultra-low-latency requirements. To address this challenge, we design a blockchain-based data storage architecture, focusing on the design of delay-aware consensus and data query algorithms. Specifically, to reduce the consensus delay, we propose a reputation-assisted and grouping-simplified Practical Byzantine Fault Tolerant (RGS-PBFT) algorithm. First, we develop a novel node credit value evaluation model by combining the behavior and configuration reputation to select the primary node with both high security and high computing capacity. Second, after determining the primary node, we reduce the communication complexity of traditional PBFT from O(Z2) to O(Z43) by establishing a grouping-simplified consensus structure. These contributions effectively reduce the consensus delay. To reduce the query delay and index construction time, we propose an index query algorithm based on the data tracking chain (DTC-Index query). By introducing the LevelDB database, it establishes a transaction index chain coupled to vehicle ID in the blockchain. This approach avoids the time-consuming block traversal during data querying. Experimental results demonstrate that the proposed algorithms outperform existing solutions, achieving significant improvements in both consensus delay and query time reduction. Notably, our solutions offer exceptional delay performance, making them well-suited for IoV environments.

QWLCPM: A Method for QoS-Aware Forwarding and Caching Using Simple Weighted Linear Combination and Proximity for Named Data Vehicular Sensor Network

The named data vehicular sensor network (NDVSN) has become an increasingly important area of research because of the increasing demand for data transmission in vehicular networks. In such networks, ensuring the quality of service (QoS) of data transmission is essential. The NDVSN is a mobile ad hoc network that uses vehicles equipped with sensors to collect and disseminate data. QoS is critical in vehicular networks, as the data transmission must be reliable, efficient, and timely to support various applications. This paper proposes a QoS-aware forwarding and caching algorithm for NDVSNs, called QWLCPM (QoS-aware Forwarding and Caching using Weighted Linear Combination and Proximity Method). QWLCPM utilizes a weighted linear combination and proximity method to determine stable nodes and the best next-hop forwarding path based on various metrics, including priority, signal strength, vehicle speed, global positioning system data, and vehicle ID. Additionally, it incorporates a weighted linear combination method for the caching mechanism to store frequently accessed data based on zone ID, stability, and priority. The performance of QWLCPM is evaluated through simulations and compared with other forwarding and caching algorithms. QWLCPM’s efficacy stems from its holistic decision-making process, incorporating spatial and temporal elements for efficient cache management. Zone-based caching, showcased in different scenarios, enhances content delivery by utilizing stable nodes. QWLCPM’s proximity considerations significantly improve cache hits, reduce delay, and optimize hop count, especially in scenarios with sparse traffic. Additionally, its priority-based caching mechanism enhances hit ratios and content diversity, emphasizing QWLCPM’s substantial network-improvement potential in vehicular environments. These findings suggest that QWLCPM has the potential to greatly enhance QoS in NDVSNs and serve as a promising solution for future vehicular sensor networks. Future research could focus on refining the details of its implementation, scalability in larger networks, and conducting real-world trials to validate its performance under dynamic conditions.

Intelligent High-Awareness and Channel-Efficient Adaptive Beaconing Based on Density and Distribution for Vehicular Networks

In vehicle ad hoc networks (VANETs), a beacon is a periodic message sent to nearby vehicles containing essential details like the sender’s vehicle ID, location, speed, and direction. Maintaining the freshness of this information without causing network congestion requires adaptive beaconing to adjust to changes in mobility and network density. Our research, based on extensive simulation experiments, identifies specific parameter sets optimal for adapting beaconing rates to different scenarios. From this analysis, we introduce a novel scheme called high-awareness and channel-efficient adaptive beaconing (HACEAB), employing fuzzy logic to adapt to various environments and conditions. Initially, the protocol gauges network density using an adaptive threshold function, followed by estimating the node spatial distribution through the quadrat statistic method to discern uniform distribution or clustering. Utilizing these data, the protocol adjusts beaconing rates via appropriate input parameters for the fuzzy logic system. Remarkably, HACEAB represents the first beaconing scheme capable of simultaneously adjusting to changes in network density and spatial distribution. Furthermore, the protocol enhances performance by adapting transmission power to fluctuations in node density and distribution. NS-3 simulations validate the efficacy of these improvements.

Clustering Penggunaan Fuel Pada PT Trasindo Murni Perkasa Menggunakan Algoritma K-Means

In coal mining, vehicles are used that have a large physical shape so that they can carry quite a lot of material so that it doesn't take a long time to mine. A lot of fuel is used during mining. Mining units have an identification mark in the form of a unit number as the vehicle's identity. attached to the vehicle and this unit number will later be recorded in fuel usage. Due to the large amount of fuel consumed by each unit, a lot of data is collected and needs to be processed. In this study, researchers used the clustering method with the k-means algorithm. -means Clustering begins by determining the number of clusters. This research uses two clusters. The results obtained from this grouping are, units in the day shift have a higher consumption amount, namely 217 liters per unit, while units in the shift have a total consumption of 147 liters. per unit with an ARI value of 0.5399218293207123 and an NMI value of 0.6565191143081124.

Traffic Prediction using Machine Learning

The traffic is one of the major issues faced by people in almost every city. Predicting the traffic has always been very challenging for transportation planning as well as the city manager. The growth of population and usage of vehicles has increased the need for a reliable traffic prediction system. The system should reduce congestion, avoid accidents, and optimize the traffic flow that enhance road safety. The use of machine learning algorithms would be more suitable in handling the traffic and maintaining the flow of vehicles. The paper focuses on the work related to handling the traffic using machine learning principles such as LSTM and Random Forest (RF) algorithm. The advanced online dataset for traffic forecasts is used. The system is tested to have improved accuracy with the help of feature engineering. The basic dataset had the following fields: Vehicle Id, Time, Date, Junction Id are the primary and significant parameters considered to anticipate the traffic at each zone. With this we have added the day and weekend component to enhance our result.

Phased Feature Extraction Network for Vehicle Search Tasks Based on Cross-Camera for Vehicle-Road Collaborative Perception.

The objective of vehicle search is to locate and identify vehicles in uncropped, real-world images, which is the combination of two tasks: vehicle detection and re-identification (Re-ID). As an emerging research topic, vehicle search plays a significant role in the perception of cooperative autonomous vehicles and road driving in the distant future and has become a trend in the future development of intelligent driving. However, there is no suitable dataset for this study. The Tsinghua University DAIR-V2X dataset is utilized to create the first cross-camera vehicle search dataset, DAIR-V2XSearch, which combines the cameras at both ends of the vehicle and the road in real-world scenes. The primary purpose of the current search network is to address the pedestrian issue. Due to varying task scenarios, it is necessary to re-establish the network in order to resolve the problem of vast differences in different perspectives caused by vehicle searches. A phased feature extraction network (PFE-Net) is proposed as a solution to the cross-camera vehicle search problem. Initially, the anchor-free YOLOX framework is selected as the backbone network, which not only improves the network's performance but also eliminates the fuzzy situation in which multiple anchor boxes correspond to a single vehicle ID in the Re-ID branch. Second, for the vehicle Re-ID branch, a camera grouping module is proposed to effectively address issues such as sudden changes in perspective and disparities in shooting under different cameras. Finally, a cross-level feature fusion module is designed to enhance the model's ability to extract subtle vehicle features and the Re-ID's precision. Experiments demonstrate that our proposed PFE-Net achieves the highest precision in the DAIR-V2XSearch dataset.

An Amalgamated Novel IDS Model for Misbehaviour Detection using VeReMiNet

Vehicle ad hoc networks (VANETs) are gaining popularity because they can potentially increase traffic efficiency with road safety in cooperative intelligent transport systems (C-ITS). Effective vehicle deployment requires the identification of nodes providing false data. Researchers have focused much of their attention on an important issue called misbehaviour detection for disseminating false data about vehicle information (position, time, speed and vehicle id) in VANETs. It can threaten the security and privacy of the network and cause some problems, including accidents, collisions, and traffic congestion. There are more works have been proposed based on machine learning methods. However, all of the machine learning-based detection models used currently are signature-based, necessitating previous knowledge of the attacks to identify them and also requiring numerous features, which increases the computational time needed to detect attacks and to select the high-performing features; this works a marine predator algorithm based on teaching-learning. The article provides a unique deep learning strategy to recognize the position falsification attacks in VANETs to find hidden patterns from the data for fighting against known and emerging threats. We evaluate our method using a publicly accessible VeReMi dataset, demonstrating that it achieves high detection accuracy of 99.55% for various attacks. According to our findings, our method can be useful for enhancing VANET security and assuring vehicular communications' reliable and secure functioning.

Intelligent energy management strategy for hybrid electric vehicles using reinforcement learning

ABSTRACT Predicting the performance of a Hybrid EV (Electric Vehicle) is significant to alleviate the complications of insufficient fuel and other battery-related problems that affect the driving capacity. Many energy management techniques with ML (Machine Learning) and DL (Deep Learning) methodologies have been deployed in predicting the operation of vehicles based on energy sources and their features. Still, it is being noticed that prediction errors have increased along with the increase or decrease of input data. To solve the challenges addressed in existing studies, the proposed approach intends to use both ML and DL to predict faults from battery features that considerably manage the energy. It is accomplished through efficient feature extraction employed by Bi-LSTM (Bidirectional Long Short Term Memory), which extracts the best energy features required for predicting the defects. From the extracted input features collected hourly, daily, and monthly from the dataset, classification is done using the Modified XGBoost Algorithm with Empirical Loss function. Normal and Abnormal functioning of the Hybrid EV is classified effectively. Followed by that, the prediction of improper functioning is made with the assistance of the vehicle Id to maximise the vehicle’s performance through consideration of battery constraints. Experimentation is performed with MATLAB simulation, and the performance of the proposed system is evaluated using error metrics like RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MSE (Mean Square Error), and accuracy. Efficiency is exhibited through the minimum loss obtained through the proposed work. From analysis of comparing LSTM method with proposed model, it is found that the LSTM method produced RMSE value of 1.055% which is 0.84% reduced in proposed model. On the other hand, the MAE of LSTM is 0.826% and the proposed model is decreased with 0.174%. Further, the MSE of LSTM is 1.113% and is 0.69% reduced in proposed model.

Impact of endorsement policy on the performance of blockchain‐based VANET

AbstractWith the revolution in technology, VANET has emerged as a new generation of technology and used globally for intelligent traffic management systems. VANET uses a PKI‐based protocol to maintain privacy and security. Still, some crucial issues remain unresolved, like blind belief in communicated messages, lack of vehicle's interest in participation, revealing the sender vehicle's identity, and maintaining integrity. The solution to these issues will prevent vehicles from propagating fake messages while concurrently protecting the confidentiality of each involved vehicle against tracking attacks. Blockchain, a peer‐to‐peer, immutable, distributed ledger‐based technology, can be adapted as a robust solution to overcome critical challenges and make VANET more secure and reliable. This paper proposes a novel blockchain‐based VANET architecture, blockchain‐based vehicular ad‐hoc network (B‐VANET), implemented on the Hyperledger Fabric platform. B‐VANET aims to secure the network by validating road events before actually updating it on the network. For validation, the trust factor of each vehicle and their past involvement in the network is maintained. Trust factor value decides the trustworthiness of a vehicle. Based on this trust factor, a dynamic endorsement policy is designed. Endorsement policy, part of the smart contract, plays a vibrant role in Hyperledger Fabric. It decides how many and which particular peers can endorse the transaction to make it valid and add it to the ledger. Multiple possible approaches to endorsement policies are identified for B‐VANET and are analyzed using the Hyperledger Caliper tool. These approaches are evaluated for data integrity, confidentiality, and real‐time system issues. An appropriate approach for the B‐VANET system is recognized, giving the best throughput and latency in all possible scenarios. It is analyzed that compared to the worst approach, the best approach achieves an almost 15% increase in throughput and reduces latency by nearly half. We also compared the performance of B‐VANET and other approaches to show that our proposed system better meets VANET's security needs.

Inferring the number of vehicles between trajectory-observed vehicles

Traffic perception is the foundation of intelligent roads, and how to accurately perceive traffic has become a central issue for researchers. With the application of Vehicle-to-Everything communication technology, vehicle IDs, locations, velocities, and accelerations can be obtained by the Roadside Unit (RSU), i.e., trajectory-observed vehicles for the road. Inferring the number of vehicles between trajectory-observed vehicles can make traffic perception more accurate, with which the traffic can be sensed on the whole road. Thus, in the case of mixed traffic flow, a Real-Time Prediction Model was proposed, which is a novel model containing four modules: prior experience of the space headway, linear distribution of velocity and acceleration, identification of traffic shockwave, and filter. The inferred result was calculated in real time. During the test, we used US-101 lane-1 data of the Next Generation Simulation dataset and trajectory-observed vehicles with stochastic distribution for 20% penetration. The length of the study area on the US-101 highway was approximately 2100 feet, which was similar to the communication area of a single RSU. During the evaluation of the model accuracy with the real-world datasets, the error of the inferred vehicle numbers in the study area could be limited to ±5 vehicles almost. Results show that it is feasible to infer the number of vehicles between trajectory-observed vehicles. The model compensates for the shortcomings of traditional models (based on inductive loop, camera, or radar), thus providing a novel method for the traffic perception of intelligent roads.

Vehicle Logo Recognition Using Spatial Structure Correlation and YOLO-T.

The vehicle logo contains the vehicle's identity information, so vehicle logo detection (VLD) technology has extremely important significance. Although the VLD field has been studied for many years, the detection task is still difficult due to the small size of the vehicle logo and the background interference problem. To solve these problems, this paper proposes a method of VLD based on the YOLO-T model and the correlation of the vehicle space structure. Aiming at the small size of the vehicle logo, we propose a vehicle logo detection network called YOLO-T. It integrates multiple receptive fields and establishes a multi-scale detection structure suitable for VLD tasks. In addition, we design an effective pre-training strategy to improve the detection accuracy of YOLO-T. Aiming at the background interference, we use the position correlation between the vehicle lights and the vehicle logo to extract the region of interest of the vehicle logo. This measure not only reduces the search area but also weakens the background interference. We have labeled a new vehicle logo dataset named LOGO-17, which contains 17 different categories of vehicle logos. The experimental results show that our proposed method achieves high detection accuracy and outperforms the existing vehicle logo detection methods.

Enhanced Traffic Management for Emergency Vehicle Information Transmission using Wireless Sensor Networks

Considerable research has been conducted in over a decade on traffic management systems that utilize Wireless Sensor Networks (WSNs) to mitigate congestion and prioritize emergency vehicles. The traffic management is becoming a wide interesting area for both academic and industrial researchers. The real-time traffic management is a dynamic scheme and is very challenging to provide an accurate signalling time and priority for a specific vehicle. This paper introduces a novel emergency vehicle information passing system that utilizes Radio Frequency (RF) sensors. This research study presents an innovative system for transmitting emergency vehicle information, which makes use of Radio Frequency (RF) sensors. The system effectively transmits crucial data, such as vehicle ID, approaching direction, mileage driven, and destination time. By doing so, it enables other vehicles to allocate appropriate space and facilitate smoother passage for emergency vehicles. The main intention of this approach is to improve the communication speed among the nodes and to reduce the response time. The communication among the nodes is done with different frequencies to enhance the method's effectiveness. We also propose a priority-based MAC (PMAC), which guarantees a slot allocation for emergency message transmission in the network. The effectiveness of the proposed approach is assessed through simulation using NS-2. The findings emphasize the effectiveness of the RF sensor when it comes to its ability to respond quickly and showcasing the PMAC's capability to reduce end-to-end delay.

Weakly-Supervised Part-Attention and Mentored Networks for Vehicle Re-Identification

Vehicle re-identification (Re-ID) aims to retrieve images with the same vehicle ID across different cameras. Current part-level feature learning methods typically detect vehicle parts via uniform division, outside tools, or attention modeling. However, such part features often require expensive additional annotations and cause sub-optimal performance in case of unreliable part mask predictions. In this paper, we propose a weakly-supervised Part-Attention Network (PANet) and Part-Mentored Network (PMNet) for Vehicle Re-ID. Firstly, PANet localizes vehicle parts via part-relevant channel recalibration and cluster-based mask generation without vehicle part supervisory information. Secondly, PMNet leverages teacher-student guided learning to distill vehicle part-specific features from PANet and performs multi-scale global-part feature extraction. During inference, PMNet can adaptively extract discriminative part features without part localization by PANet, preventing unstable part mask predictions. We address this Re-ID issue as a multi-task problem and adopt Homoscedastic Uncertainty to learn the optimal weighing of ID losses. Experiments are conducted on two public benchmarks, showing that our approach outperforms recent methods, which require no extra annotations by an average increase of 3.0% in CMC@5 on VehicleID and over 1.4% in mAP on VeRi776. Moreover, our method can extend to the occluded vehicle Re-ID task and exhibits good generalization ability.

A comprehensive pseudonym changing scheme for improving location privacy in vehicular networks

In an Intelligent Transportation System (ITS), many applications, such as collision avoidance and lane change warning, require real-time information from vehicles on the road. This typically includes detailed status information such as location, speed, heading and a vehicle identifier, which can be used to infer precise movement patterns leading to long-term driver profiling and vehicle tracking. The use of pseudonyms, instead of actual vehicle IDs, allows vehicles to protect their privacy while sharing relevant information. A pseudonym changing scheme (PCS) determines when and how a vehicle should change its pseudonyms to maintain an appropriate privacy level. In this paper, we propose a new comprehensive PCS that utilizes vehicle context and current traffic patterns to leverage the optimum situation for changing pseudonyms. Simulation results indicate that the proposed PCS outperforms existing approaches both in terms of user-centric and adversary-centric performance metrics.

Micromobility Trip Origin and Destination Inference Using General Bikeshare Feed Specification Data

Emerging micromobility services (e.g., e-scooters) have a great potential to enhance urban mobility but more knowledge on their usage patterns is needed. The General Bikeshare Feed Specification (GBFS) data are a possible source for examining micromobility trip patterns, but efforts are needed to infer trips from the GBFS data. Existing trip inference methods are usually based on the assumption that the vehicle identity (ID) of a micromobility option (e-scooter or e-bike) does not change, and so they cannot deal with data with vehicle IDs that change over time. This paper proposes a comprehensive package of algorithms to infer origin–destination (OD) pairs from GBFS data with static vehicle ID and unlinked trip origins and destinations from GBFS data with resetting and dynamic vehicle ID. The algorithms were implemented in Washington D.C. by analyzing one week (last week of February 2020) of GBFS data published by six vendors, and the inference accuracy of the proposed algorithms are evaluated by R-squared, mean absolute error, and sum absolute error. It is found that the R-squared measure is larger than 0.9 and the MAE measure is less than 2 when the algorithms are evaluated with a 400 m × 400 m grid. The absolute errors are relatively larger in the downtown area, and the inference error is relatively high during early morning and early nighttime. The accuracy of the trip inference algorithms is sufficiently high for most practical applications.