arXiv subjects

Lei Lin

Publications and source records attributed to Lei Lin.

49 records · Page 3Linked to original sources

arXiv2018

Car-following behavior of connected vehicles in a mixed traffic flow: modeling and stability analysis

Vehicle-to-vehicle communications can change the driving behavior of drivers significantly by providing them rich information on downstream traffic flow conditions. This study seeks to model the varying car-following behaviors involving connected vehicles and human-driving vehicles in mixed traffic flow. A revised car-following model is developed using an intelligent driver model (IDM) to capture drivers' perceptions of their preceding traffic conditions through vehicle-to-vehicle communications. Stability analysis of the mixed traffic flow is conducted for a specific case. Numerical results show that the stable region is apparently enlarged compared with the IDM.

eess.SY↗

arXiv2018

A Comprehensive Framework for Dynamic Bike Rebalancing in a Large Bike Sharing Network

Bike sharing is a vital component of a modern multi-modal transportation system. However, its implementation can lead to bike supply-demand imbalance due to fluctuating spatial and temporal demands. This study proposes a comprehensive framework to develop optimal dynamic bike rebalancing strategies in a large bike sharing network. It consists of three components, including a station-level pick-up/drop-off prediction model, station clustering model, and capacitated location-routing optimization model. For the first component, we propose a powerful deep learning model called graph convolution neural network model (GCNN) with data-driven graph filter (DDGF), which can automatically learn the hidden spatial-temporal correlations among stations to provide more accurate predictions; for the second component, we apply a graph clustering algorithm labeled the Community Detection algorithm to cluster stations that locate geographically close to each other and have a small net demand gap; last, a capacitated location-routing problem (CLRP) is solved to deal with the combination of two types of decision variables: the locations of bike distribution centers and the design of distribution routes for each cluster.

physics.soc-ph↗

arXiv2018

Efficient Collection of Connected Vehicle Data based on Compressive Sensing

Connected vehicles (CVs) can capture and transmit detailed data like vehicle position, speed and so on through vehicle-to-vehicle and vehicle-to-infrastructure communications. The wealth of CV data provides new opportunities to improve the safety, mobility, and sustainability of transportation systems. However, the potential data explosion likely will overburden storage and communication systems. To solve this issue, we design a real-time compressive sensing (CS) approach which allows CVs to collect and compress data in real-time and can recover the original data accurately and efficiently when it is necessary. The CS approach is applied to recapture 10 million CV Basic Safety Message speed samples from the Safety Pilot Model Deployment program. With a compression ratio of 0.2, it is found that the CS approach can recover the original speed data with the root mean squared error as low as 0.05. The recovery performances of the CS approach are further explored by time-of-day and acceleration. The results show that the CS approach performs better in data recovery when CV speeds are steady or changing smoothly.

stat.AP↗

arXiv2017

Reliability and Sharpness in Border Crossing Traffic Interval Prediction

Short-term traffic volume prediction models have been extensively studied in the past few decades. However, most of the previous studies only focus on single-value prediction. Considering the uncertain and chaotic nature of the transportation system, an accurate and reliable prediction interval with upper and lower bounds may be better than a single point value for transportation management. In this paper, we introduce a neural network model called Extreme Learning Machine (ELM) for interval prediction of short-term traffic volume and improve it with the heuristic particle swarm optimization algorithm (PSO). The hybrid PSO-ELM model can generate the prediction intervals under different confidence levels and guarantee the quality by minimizing a multi-objective function which considers two criteria reliability and interval sharpness. The PSO-ELM models are built based on an hourly traffic dataset and compared with ARMA and Kalman Filter models. The results show that ARMA models are the worst for all confidence levels, and the PSO-ELM models are comparable with Kalman Filter from the aspects of reliability and narrowness of the intervals, although the parameters of PSO-ELM are fixed once the training is done while Kalman Filter is updated in an online approach. Additionally, only the PSO-ELMs are able to produce intervals with coverage probabilities higher than or equal to the confidence levels. For the points outside of the prediction levels given by PSO-ELMs, they lie very close to the bounds.

cs.NE↗

arXiv2017

Bi-National Delay Pattern Analysis For Commercial and Passenger Vehicles at Niagara Frontier Border

Border crossing delays between New York State and Southern Ontario cause problems like enormous economic loss and massive environmental pollutions. In this area, there are three border-crossing ports: Peace Bridge (PB), Rainbow Bridge (RB) and Lewiston-Queenston Bridge (LQ) at Niagara Frontier border. The goals of this paper are to figure out whether the distributions of bi-national wait times for commercial and passenger vehicles are evenly distributed among the three ports and uncover the hidden significant influential factors that result in the possible insufficient utilization. The historical border wait time data from 7:00 to 21:00 between 08/22/2016 and 06/20/2017 are archived, as well as the corresponding temporal and weather data. For each vehicle type towards each direction, a Decision Tree is built to identify the various border delay patterns over the three bridges. We find that for the passenger vehicles to the USA, the convenient connections between the Canada freeways with USA I-190 by LQ and PB may cause these two bridges more congested than RB, especially when it is a holiday in Canada. For the passenger vehicles in the other bound, RB is much more congested than LQ and PB in some cases, and the visitors to Niagara Falls in the USA in summer may be a reason. For the commercial trucks to the USA, the various delay patterns show PB is always more congested than LQ. Hour interval and weekend are the most significant factors appearing in all the four Decision Trees. These Decision Trees can help the authorities to make specific routing suggestions when the corresponding conditions are satisfied.

cs.CY↗

arXiv2017

Real-time Traffic Accident Risk Prediction based on Frequent Pattern Tree

Traffic accident data are usually noisy, contain missing values, and heterogeneous. How to select the most important variables to improve real-time traffic accident risk prediction has become a concern of many recent studies. This paper proposes a novel variable selection method based on the Frequent Pattern tree (FP tree) algorithm. First, all the frequent patterns in the traffic accident dataset are discovered. Then for each frequent pattern, a new criterion, called the Relative Object Purity Ratio (ROPR) which we proposed, is calculated. This ROPR is added to the importance score of the variables that differentiate one frequent pattern from the others. To test the proposed method, a dataset was compiled from the traffic accidents records detected by only one detector on interstate highway I-64 in Virginia in 2005. This dataset was then linked to other variables such as real-time traffic information and weather conditions. Both the proposed method based on the FP tree algorithm, as well as the widely utilized, random forest method, were then used to identify the important variables or the Virginia dataset. The results indicate that there are some differences between the variables deemed important by the FP tree and those selected by the random forest method. Following this, two baseline models (i.e. a nearest neighbor (k-NN) method and a Bayesian network) were developed to predict accident risk based on the variables identified by both the FP tree method and the random forest method. The results show that the models based on the variable selection using the FP tree performed better than those based on the random forest method for several versions of the k-NN and Bayesian network models.The best results were derived from a Bayesian network model using variables from FP tree. That model could predict 61.11% of accidents accurately while having a false alarm rate of 38.16%.

stat.AP↗

arXiv2017

Abnormal Spatial-Temporal Pattern Analysis for Niagara Frontier Border Wait Times

Border crossing delays cause problems like huge economics loss and heavy environmental pollutions. To understand more about the nature of border crossing delay, this study applies a dictionary-based compression algorithm to process the historical Niagara Frontier border wait times data. It can identify the abnormal spatial-temporal patterns for both passenger vehicles and trucks at three bridges connecting US and Canada. Furthermore, it provides a quantitate anomaly score to rank the wait times patterns across the three bridges for each vehicle type and each direction. By analyzing the top three most abnormal patterns, we find that there are at least two factors contributing the anomaly of the patterns. The weekends and holidays may cause unusual heave congestions at the three bridges at the same time, and the freight transportation demand may be uneven from Canada to the USA at Peace Bridge and Lewiston-Queenston Bridge, which may lead to a high anomaly score. By calculating the frequency of the top 5% abnormal patterns by hour of the day, the results show that for cars from the USA to Canada, the frequency of abnormal waiting time patterns is the highest during noon while for trucks in the same direction, it is the highest during the afternoon peak hours. For Canada to US direction, the frequency of abnormal border wait time patterns for both cars and trucks reaches to the peak during the afternoon. The analysis of abnormal spatial-temporal wait times patterns is promising to improve the border crossing management

cs.AI↗

arXiv2016

Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention

In this paper, we proposed a sentence encoding-based model for recognizing text entailment. In our approach, the encoding of sentence is a two-stage process. Firstly, average pooling was used over word-level bidirectional LSTM (biLSTM) to generate a first-stage sentence representation. Secondly, attention mechanism was employed to replace average pooling on the same sentence for better representations. Instead of using target sentence to attend words in source sentence, we utilized the sentence's first-stage representation to attend words appeared in itself, which is called "Inner-Attention" in our paper . Experiments conducted on Stanford Natural Language Inference (SNLI) Corpus has proved the effectiveness of "Inner-Attention" mechanism. With less number of parameters, our model outperformed the existing best sentence encoding-based approach by a large margin.

cs.CL↗

arXiv2014

Radical-Enhanced Chinese Character Embedding

We present a method to leverage radical for learning Chinese character embedding. Radical is a semantic and phonetic component of Chinese character. It plays an important role as characters with the same radical usually have similar semantic meaning and grammatical usage. However, existing Chinese processing algorithms typically regard word or character as the basic unit but ignore the crucial radical information. In this paper, we fill this gap by leveraging radical for learning continuous representation of Chinese character. We develop a dedicated neural architecture to effectively learn character embedding and apply it on Chinese character similarity judgement and Chinese word segmentation. Experiment results show that our radical-enhanced method outperforms existing embedding learning algorithms on both tasks.

cs.CL↗

arXiv2013

Accessible Capacity of Secondary Users

A new problem formulation is presented for the Gaussian interference channels (GIFC) with two pairs of users, which are distinguished as primary users and secondary users, respectively. The primary users employ a pair of encoder and decoder that were originally designed to satisfy a given error performance requirement under the assumption that no interference exists from other users. In the scenario when the secondary users attempt to access the same medium, we are interested in the maximum transmission rate (defined as {\em accessible capacity}) at which secondary users can communicate reliably without affecting the error performance requirement by the primary users under the constraint that the primary encoder (not the decoder) is kept unchanged. By modeling the primary encoder as a generalized trellis code (GTC), we are then able to treat the secondary link and the cross link from the secondary transmitter to the primary receiver as finite state channels (FSCs). Based on this, upper and lower bounds on the accessible capacity are derived. The impact of the error performance requirement by the primary users on the accessible capacity is analyzed by using the concept of interference margin. In the case of non-trivial interference margin, the secondary message is split into common and private parts and then encoded by superposition coding, which delivers a lower bound on the accessible capacity. For some special cases, these bounds can be computed numerically by using the BCJR algorithm. Numerical results are also provided to gain insight into the impacts of the GTC and the error performance requirement on the accessible capacity.

cs.IT↗

arXiv2013

An information spectrum approach to the capacity region of GIFC

In this paper, we present a general formula for the capacity region of a general interference channel with two pairs of users. The formula shows that the capacity region is the union of a family of rectangles, where each rectangle is determined by a pair of spectral inf-mutual information rates. Although the presented formula is usually difficult to compute, it provides us useful insights into the interference channels. In particular, when the inputs are discrete ergodic Markov processes and the channel is stationary memoryless, the formula can be evaluated by BCJR algorithm. Also the formula suggests us that the simplest inner bounds (obtained by treating the interference as noise) could be improved by taking into account the structure of the interference processes. This is verified numerically by computing the mutual information rates for Gaussian interference channels with embedded convolutional codes. Moreover, we present a coding scheme to approach the theoretical achievable rate pairs. Numerical results show that decoding gain can be achieved by considering the structure of the interference.

cs.IT↗

arXiv2012

An Information-Spectrum Approach to the Capacity Region of General Interference Channel

This paper is concerned with general interference channels characterized by a sequence of transition (conditional) probabilities. We present a general formula for the capacity region of the interference channel with two pairs of users. The formula shows that the capacity region is the union of a family of rectangles, where each rectangle is determined by a pair of spectral inf-mutual information rates. Although the presented formula is usually difficult to compute, it provides us useful insights into the interference channels. For example, the formula suggests us that the simplest inner bounds (obtained by treating the interference as noise) could be improved by taking into account the structure of the interference processes. This is verified numerically by computing the mutual information rates for Gaussian interference channels with embedded convolutional codes.

cs.IT↗

arXiv2010

Efficient K-Nearest Neighbor Join Algorithms for High Dimensional Sparse Data

The K-Nearest Neighbor (KNN) join is an expensive but important operation in many data mining algorithms. Several recent applications need to perform KNN join for high dimensional sparse data. Unfortunately, all existing KNN join algorithms are designed for low dimensional data. To fulfill this void, we investigate the KNN join problem for high dimensional sparse data. In this paper, we propose three KNN join algorithms: a brute force (BF) algorithm, an inverted index-based(IIB) algorithm and an improved inverted index-based(IIIB) algorithm. Extensive experiments on both synthetic and real-world datasets were conducted to demonstrate the effectiveness of our algorithms for high dimensional sparse data.

cs.DB↗