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REVIEW 3 major objections 4 minor 159 references

Simulation and Learning for Urban Mobility: City-scale Traffic Reconstruction and Autonomous Driving

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read The paper claims that sparse GPS data can reconstruct city-scale traffic when map matching follows shortest travel time rather than shortest distance, and that simulated accidents can supply training data for effective autonomous driving.

desk verdict A capable dissertation that compiles the author's own published work on traffic reconstruction and learning-based driving, with a real soft spot: the headline synthetic evaluations assume exactly the route-choice criterion the method depends on. read the letter →

arxiv 1908.06131 v1 pith:IPWZI2HC submitted 2019-08-05 cs.OH eess.SP

Weizi Li This is my paper · ORCID
classification cs.OHeess.SP
keywords city-scaletrafficreconstructionGPStracemapmatchingtraveltimeestimationcompressedsensingsimulationoptimizationautonomousdrivingimitationlearningDAGGER
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This dissertation attempts to show that city-scale traffic can be estimated from sparse GPS data by matching traces to fastest paths rather than shortest-distance paths, and that autonomous driving policies can be learned from simulated accidents rather than from real-world incidents. The traffic pipeline replaces shortest-distance map matching with a relaxation scheme that reconstructs travel times under Wardrop's user-equilibrium assumption, then fills temporal gaps with compressed sensing and spatial gaps with metamodel-based simulation optimization; in the reported tests, recovered travel-time error drops to a mean of $3.40$ s versus $29.55$ s for the prior baseline at 80% trace coverage. The driving framework, ADAPS, uses a simulated expert to plan and label safe avoidance trajectories after an accident, and the paper proves a sample-complexity bound for this learning process. If the claims hold, citywide traffic monitoring becomes feasible with data from a small fraction of vehicles, and dangerous driving scenarios can be practiced virtually instead of on the road.

What carries the argument

The reconstruction pipeline's load-bearing object is the relaxation algorithm: for each pair of consecutive GPS points it extracts a sub-network, finds candidate fastest paths, and repeatedly slows down road segments on overestimated paths until every candidate path's travel time equals the timestamp difference, an operation whose correctness rests on Wardrop's user-equilibrium assumption that all traces are fastest routes. Travel-time allocation along each path uses a decomposition into free-flow, congestion, and stopping times adapted from Hellinga et al., and the monotone decrease of speeds (Theorem 2.1) keeps the search efficient. Temporal missing data are handled by treating the weekly speed pattern of a road segment as a sparse signal in the DCT domain and recovering it by $\ell^1$-minimization, a compressed-sensing step that needs about 90 measurements instead of 168. Spatial missing data are completed by a trust-region optimization over intersection turning ratios, using a metamodel that blends a physical flow-conservation model with a quadratic polynomial so simulated flows obey the boundary flows estimated from GPS data. The driving side is carried by SimExpert, the simulated expert that plans many alternative safe trajectories for a vehicle after an accident and assigns DANGER/SAFE labels to states, automatically generating the heterogeneous training set for the hierarchical LSTM policy.

What would settle it

Give the algorithm a congested urban network with known ground-truth travel times and a set of GPS traces whose true routes are known from high-frequency onboard logging. If a substantial fraction of traces follow routes that are not the fastest available (shortest-distance or habitual choices), the recovered travel times should be compared against the ground truth: the central claim would fail if the mean absolute error grows to the level of the shortest-distance baseline or worse as the fraction of non-fastest traces increases. A simpler probe: on such a dataset, check whether the relaxation's inferred network speeds are systematically higher on routes that the fastest-path assumption actually penalizes.

Watch

Extended reading notes

Core claim

The central claim is that the speed of every road segment in a city can be recovered from GPS traces that are sparse in space and time, provided map matching uses estimated shortest travel times rather than shortest distances. Starting from a network at free-flow speed, the relaxation algorithm takes each trace's timestamp difference as the true travel time of the fastest path, and repeatedly increases travel times on overestimated candidate paths until they match it; because road-segment speeds only decrease during this process, the procedure is tractable and terminates. The dissertation reports that this approach, followed by compressed-sensing completion of missing time intervals and metamodel-driven flow completion for uncovered areas, reconstructs travel times with mean absolute error of $3.40$ s (std. $3.32$) at 80% trace penetration, versus $29.55$ s (std. $22.46$) for a shortest-distance baseline, and yields up to 97% relative mean-squared-error improvement over two state-of-the-art travel-time estimators. For autonomous driving, the paper claims that a 2D simulated expert which plans alternative safe trajectories under kinematic and dynamic constraints can turn a simulated accident into a large set of labeled recovery examples, and that a hierarchical detection/following/avoidance policy trained on these examples avoids obstacles in on-road and off-road scenarios where an end-to-end baseline fails. The theoretical guarantee bounds the learned policy's cost-to-go by $J(\hat{\pi})\le T\hat{\epsilon}_{\min}+T\hat{\epsilon}_{\mathrm{regret}}+O(Tl_{\max}\sqrt{\log(1/\mu)/(KN)})$.

Load-bearing premise

The framework assumes that every GPS trace follows the fastest path under the true traffic conditions, so the observed timestamp difference is the minimum travel time between the two GPS points; if drivers frequently take shortest-distance or habitual routes instead, the relaxation will systematically distort the reconstructed travel times.

Editorial extensions

If this is right

  • Citywide traffic monitoring becomes possible from low-penetration GPS fleets: the reported mean travel-time error stays between about $3.4$ and $8.3$ s across 20% to 100% trace penetration, far below the shortest-distance baseline.
  • Temporal gaps in GPS coverage can be filled by exploiting weekly periodicity: compressed sensing recovers a traffic signal with roughly 90 measurements, about half of what the Nyquist rate requires, and error grows only linearly as samples decrease.
  • Areas without GPS coverage can be reconstructed by tuning simulation turning ratios with a metamodel, achieving roughly 27x average speedup over a simulation-only approach while keeping about 7.8% average error on the tested networks.
  • A single simulated accident can generate a large, heterogeneous batch of labeled recovery trajectories, and the ADAPS bound shows the policy cost converges to $T\hat{\epsilon}_{\min}+O(\epsilon)$ once $O(T^2l_{\max}^2\log(1/\mu)/\epsilon^2)$ examples are collected.
  • The hierarchical detect-follow-avoid policy trained this way reaches 100% success in 50-run tests on straight and curved roads with a static obstacle, while the compared end-to-end policy fails in both cases.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The reliance on a single routing criterion is the most exposed point; a natural extension is to model drivers as choosing among several criteria (fastest, shortest, habitual) and estimate the mixing proportions jointly with travel times, which the relaxation machinery could support without changing its core loop.
  • The compressed-sensing component transfers beyond traffic: any periodic spatiotemporal field with a few dominant frequencies and sparse mobile measurements could be completed by the same $\ell^1$-recovery scheme, provided the coherence condition holds.
  • The ADAPS theoretical guarantee presumes the simulation can place the agent in arbitrary states; physical deployment would likely require bridging the sim-to-real gap through domain randomization or fine-tuning, which the dissertation does not address.
  • The 27x speedup reported for metamodel-based completion points toward real-time traffic reconstruction and interactive urban visualization, but the dissertation does not demonstrate real-time update rates.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This dissertation presents a two-part framework for urban mobility. The first part addresses city-scale traffic reconstruction from sparse GPS traces, proposing a shortest-travel-time map-matching and relaxation algorithm, a compressed-sensing method for temporal missing data, an iterative estimation procedure, and a metamodel-based simulation optimization for spatial completion. The second part proposes ADAPS, a simulation-based framework for learning autonomous driving policies from accident recovery demonstrations, with a hierarchical control policy and a DAGGER-style theoretical guarantee. The traffic reconstruction is evaluated against Lou et al., Hunter et al., and Rahmani et al. on synthetic GPS traces and on the Cabspotting dataset; the ADAPS policy is tested in straight-road, curved-road, and open-ground simulated scenarios. The central quantitative claims are that the proposed methods yield consistently lower travel-time estimation errors and robust collision avoidance.

Significance. The dissertation addresses an important problem with a principled approach: replacing shortest-distance map matching by shortest travel-time matching is well motivated by Wardrop's user equilibrium, and the relaxation algorithm is an original way to exploit the lower-bound structure. The compressed-sensing analysis of traffic signal sparsity is solid, and the metamodel-based simulation optimization is an interesting contribution for spatial completion. For autonomous driving, using simulated accidents to generate recovery data is a creative and potentially impactful idea, and the theoretical bound in Theorem 1.1 transfers the DAGGER-style guarantee to a generative setting. The experimental work is extensive, and the dissertation includes formal statements (Theorem 2.1, Corollaries 2–3, Theorem 1.1) that are clearly derived. However, as detailed in the major comments, the evaluation of the traffic reconstruction is self-consistent rather than probative for real-world route choice, which limits the strength of the headline claims.

major comments (3)
  1. [§2.3.2, §2.3.4, §3.6.1.4] The central evaluation of the traffic reconstruction is performed on synthetic GPS traces generated with the same routing rule that the method assumes: Assumption 1 (§2.3.2) states that all GPS traces are planned using the shortest travel-time criterion, and §2.3.4 states that 'All traces are simulated by randomly selecting nodes... using the fastest route strategy'; §3.6.1.4 repeats this with 'routing with the shortest travel-time strategy.' Consequently, Tables 1.1/2.1 and Figures 3.4–3.7 demonstrate that the method works when the world obeys Assumption 1, but they do not test route-choice behaviors that violate it (e.g., shortest-distance or stochastic routes). Since the relaxation algorithm relies on the lower-bound property of Corollary 2, a mismatch between Assumption 1 and real driver behavior could systematically bias travel-time estimates and map-matched paths. The real-data evaluation (§2.5, §3.6.3) is qualitative: it shows recovered daily periodicity, day-to-day cosine similarities, and a roughly 1 m/s speed loss against three loop detectors after frequency filtering, but it does not validate road-segment-level travel-time accuracy or map-matching correctness. I recommend adding synthetic experiments with alternative route-choice models and, if possible, validating on real trajectories with known ground truth.
  2. [§2.4, Eqs. (2.8)–(2.10)] The compressed-sensing recovery is validated on loop-detector signals using random row-permutation sampling (§2.4: 'I test the performance of my method by first obtaining random measurements via sampling...'). However, the missingness in real GPS data is structured: some time intervals (e.g., early-morning hours) are systematically underrepresented, and the set of available hourly measurements per road segment is determined by traffic coverage rather than by a random process. The bound m_min = μ²S log(n) and the robustness result in Figure 2.10 are derived under random sampling conditions, so their validity for the GPS application is not established. I recommend testing the recovery under structured missing patterns (e.g., removing entire time blocks) or otherwise qualifying the claim that the minimum number of measurements for GPS-based recovery is 90.
  3. [§4.7, Table 4.2] The ADAPS evaluation is limited to three simple simulated scenarios (straight road, curved road, and open ground), and the success metric in Table 4.2 is the fraction of collision-free laps or obstacle-avoidance runs. The thesis statement (§1.4) claims 'robust control policies for autonomous driving,' but no experiments address unseen road geometries, varying traffic densities, sensor noise, or environmental variations (lighting, weather), nor is the learned policy compared with a DAGGER-trained policy in terms of cost-to-go or number of iterations. The theoretical bound in Theorem 1.1 is DAGGER-style and does not by itself establish robustness in the tested sense. The authors should either broaden the empirical validation or explicitly scope the robustness claim to the simulated test scenarios.
minor comments (4)
  1. [§2.3] There are several typos: 'noval' should be 'novel' in the first paragraph of Section 2.3, 'acutual' appears in Equation 3.28, and 'Collorary' is repeatedly used instead of 'Corollary' in Section 2.3.2.
  2. [§3.4.1] In the Wardrop constraints, the notation 't(s1,t 2)' should read 't(s1,s 2)' to denote the travel time between the two GPS points.
  3. [§2.3.3, Eq. (2.2)] The weighting factor β in the stopping likelihood function is set to 0.5 without a sensitivity analysis or justification; a brief study of the effect of β on travel-time allocation would strengthen the results.
  4. [§1.3.1.3] The introduction states 'up to 97% relative improvement' and 'up to 90 times speedup'; these claims are consistent with the figures, but the figure captions should identify the baselines (Hunter et al., Rahmani et al., simulation-only) more explicitly for the reader.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation found; synthetic evaluation is assumption-congruent but predicted quantities are not equivalent to inputs by construction.

full rationale

The derivation chain is not circular. Assumption 1 in Section 2.3.2 (\u201call GPS traces are planned using the shortest travel time criterion\u201d) is explicitly stated as a modeling assumption; it is not a conclusion derived from the target outputs. The map-matching and travel-time allocation procedures operate on GPS traces and allocate aggregate timestamp differences to road segments, and the resulting per-segment travel times are evaluated against ground-truth travel times from SUMO, the system-optimal model, the Timestamp model, and loop detectors. These external benchmarks are independent of the algorithm\u2019s fitted parameters, so the claimed improvements over Lou et al., Hunter et al., and Rahmani et al. are not forced by construction. \n\nThe compressed-sensing recovery (Section 2.4) estimates the sparsity level S from loop-detector signals and then uses S to compute the required number of measurements m_min before recovering the same type of signals via L1 minimization. This is a mild test-set dependence in choosing m_min, but the recovered signal is the solution of an underdetermined convex program and is not equal to the sparsity estimate; the reported errors (e.g., average error 1.4m/s) are measured on actual recovered signals, so this does not reduce the prediction to a fit. \n\nThe synthetic traces in Section 2.3.4 and Section 3.6.1.4 are generated under the same shortest-travel-time routing strategy that Assumption 1 posits. This makes the evaluation assumption-congruent and limits evidence about real-world route-choice behavior, but it does not make the output travel times a deterministic function of the input assumption; the relaxation and allocation procedures can still misestimate when routes or congestion differ. This is a validity limitation, not a circularity. \n\nThe ADAPS theoretical bound (Theorem 1.1, Section 1.3.2.1) is adapted from the DAGGER result of Ross et al. (2011) and is not imported from the author\u2019s own uniqueness theorems. The dissertation cites the author\u2019s earlier papers (Li et al. 2017a, 2017b, 2018, 2019), but those are published works providing prior support rather than load-bearing self-citations that replace independent verification. No equation or claim in the paper reduces by definition to its own inputs, so no circular step is established.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claims rely on a small number of explicitly stated modeling assumptions and hand-set parameters. No new physical entities are introduced. The most load-bearing assumption is that GPS traces follow shortest travel-time routes, which is central to the reconstruction algorithm. The empirical parameters (beta, outer iterations, sparsity level) are transparently reported and do not by themselves invalidate the claims, but they limit the generality of the results.

free parameters (3)
  • Beta (stopping likelihood weight) = 0.5
    Set by hand in Equation 2.2 to weight path congestion vs. road-segment congestion; central to the travel time allocation in Chapter 2.
  • Outer loop iterations = 10
    Set empirically based on convergence in Figure 3.3; used in Chapter 3's iterative map-matching and travel-time estimation.
  • Sparsity level S for compressed sensing = 17.63
    Estimated as the average number of frequencies retaining 95% energy from loop-detector data in Chapter 2, used to set m_min = 90.
assumptions (4)
  • domain assumption Assumption 1: All GPS traces are planned using the shortest travel-time criterion.
    Stated in Section 2.3.2 and underpins the relaxation algorithm for velocity field reconstruction; if false, the travel time bounds derived from traces are invalid.
  • domain assumption Wardrop's user equilibrium approximates real-world traffic
    Used to motivate the Wardrop constraints in Section 3.4.1 and the relaxation in Section 2.3.2; real traffic may not be at user equilibrium.
  • domain assumption Surrogate loss l upper bounds the true cost C and is convex in the policy
    Required for Theorem 1.1 and Corollary 1 in Section 4.4.1; not verified for driving.
  • domain assumption Travel time distributions of road segments are pairwise independent and Gamma distributed
    Stated in Section 3.4.2.2 following Hunter et al.; used for EM-based travel time estimation.

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Cite this review

Pith. "Pith review of Simulation and Learning for Urban Mobility: City-scale Traffic Reconstruction and Autonomous Driving." pith.science (2026). https://pith.science/paper/IPWZI2HC

@misc{pith2026190806131,
  author       = {Pith},
  title        = {Pith review of: Simulation and Learning for Urban Mobility: City-scale Traffic Reconstruction and Autonomous Driving},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IPWZI2HC}},
  note         = {Machine review of arXiv:1908.06131}
}
abstract

Traffic congestion has become one of the most critical issues worldwide. The costs due to traffic gridlock and jams are approximately $160 billion in the United States, more than {\pounds}13 billion in the United Kingdom, and over one trillion dollars across the globe annually. As more metropolitan areas will experience increasingly severe traffic conditions, the ability to analyze, understand, and improve traffic dynamics becomes critical. This dissertation is an effort towards achieving such an ability. I propose various techniques combining simulation and machine learning to tackle the problem of traffic from two perspectives: city-scale traffic reconstruction and autonomous driving.

Figures

Figures reproduced from arXiv: 1908.06131 by the authors.

Figure 1.1
Figure 1.1. Illustrations of procedures required to process GPS data for traffic estimation and reconstruction. [PITH_FULL_IMAGE:figures/full_fig_p020_1_1.png] view at source ↗
Figure 1.2
Figure 1.2. Illustration of the sparsity issue embedded in GPS data. One-day GPS data of downtown San [PITH_FULL_IMAGE:figures/full_fig_p021_1_2.png] view at source ↗
Figure 1.3
Figure 1.3. Recovery of average travel time on different percentages of the traffic population using my [PITH_FULL_IMAGE:figures/full_fig_p025_1_3.png] view at source ↗
Figures from the paper (43 more)
Figure 1.4
Figure 1.4. Figure 1.4: Relative improvements measured in MSE of my technique over Lou et al. ( [PITH_FULL_IMAGE:figures/full_fig_p027_1_4.png]
Figure 1.5
Figure 1.5. Figure 1.5: The average speed measurements from loop-detector data are interpreted as a [PITH_FULL_IMAGE:figures/full_fig_p028_1_5.png]
Figure 1.6
Figure 1.6. Figure 1.6: The top panel shows the decaying rates of frequency magnitudes of all traffic signals; the bottom [PITH_FULL_IMAGE:figures/full_fig_p029_1_6.png]
Figure 1.7
Figure 1.7. Figure 1.7: A recovered traffic signal using my technique highly resembles its original form. [PITH_FULL_IMAGE:figures/full_fig_p030_1_7.png]
Figure 1.8
Figure 1.8. Figure 1.8: My technique shows robustness when the number of samples used in recovering a traffic signal [PITH_FULL_IMAGE:figures/full_fig_p031_1_8.png]
Figure 1.9
Figure 1.9. Figure 1.9: Estimated traffic pattern of downtown San Francisco (TOP) and its spectral analysis (BOTTOM). [PITH_FULL_IMAGE:figures/full_fig_p032_1_9.png]
Figure 1.10
Figure 1.10. Figure 1.10: Correlation between every pair of days in a week. The left panel lists normalized similarity [PITH_FULL_IMAGE:figures/full_fig_p032_1_10.png]
Figure 1.11
Figure 1.11. Figure 1.11: My algorithm on map-matching and travel-time estimation achieves consistent improvements [PITH_FULL_IMAGE:figures/full_fig_p033_1_11.png]
Figure 1.12
Figure 1.12. Figure 1.12: The error level of my technique vs. simulation-only approach: For a given road network and a [PITH_FULL_IMAGE:figures/full_fig_p034_1_12.png]
Figure 1.13
Figure 1.13. Figure 1.13: The performance speedup of my technique over the simulation-only approach: my technique is [PITH_FULL_IMAGE:figures/full_fig_p034_1_13.png]
Figure 1.14
Figure 1.14. Figure 1.14: 2D traffic animation of regions in San Francisco: Northeast (top left), Central-East (top center), [PITH_FULL_IMAGE:figures/full_fig_p035_1_14.png]
Figure 1.15
Figure 1.15. Figure 1.15: 3D traffic animation: a perspective overview (left), a topdown view (center), and a driver’s view [PITH_FULL_IMAGE:figures/full_fig_p035_1_15.png]
Figure 1.16
Figure 1.16. Figure 1.16: Visualization of traffic patterns in San Francisco and Beijing. Four time periods of a week, [PITH_FULL_IMAGE:figures/full_fig_p036_1_16.png]
Figure 1.17
Figure 1.17. Figure 1.17: The estimated traffic conditions measured in average volume over capacity (VOC) of San [PITH_FULL_IMAGE:figures/full_fig_p036_1_17.png]
Figure 1.18
Figure 1.18. Figure 1.18: LEFT and CENTER: the comparisons between my policy [PITH_FULL_IMAGE:figures/full_fig_p040_1_18.png]
Figure 1.19
Figure 1.19. Figure 1.19: The visualization results of collected images using t-SNE ( [PITH_FULL_IMAGE:figures/full_fig_p041_1_19.png]
Figure 2.1
Figure 2.1. Figure 2.1: Pipeline of my framework. Map Matching and Travel Time Allocation are applied on individual [PITH_FULL_IMAGE:figures/full_fig_p045_2_1.png]
Figure 2.2
Figure 2.2. Figure 2.2: An example illustrating a failure using the shortest distance criterion for map-matching trace [PITH_FULL_IMAGE:figures/full_fig_p048_2_2.png]
Figure 2.3
Figure 2.3. Figure 2.3: An illustration of the relaxation process of two traces [PITH_FULL_IMAGE:figures/full_fig_p051_2_3.png]
Figure 2.4
Figure 2.4. Figure 2.4: Recovery of average travel time on different percentages of the traffic population using my [PITH_FULL_IMAGE:figures/full_fig_p056_2_4.png]
Figure 2.5
Figure 2.5. Figure 2.5: Relative improvements measured in MSE of my technique over Lou et al. ( [PITH_FULL_IMAGE:figures/full_fig_p057_2_5.png]
Figure 2.6
Figure 2.6. Figure 2.6: The average speed measurements from a loop detector are interpreted as a [PITH_FULL_IMAGE:figures/full_fig_p059_2_6.png]
Figure 2.7
Figure 2.7. Figure 2.7: The top panel shows the decaying rates of frequency magnitudes of all traffic signals; the bottom [PITH_FULL_IMAGE:figures/full_fig_p060_2_7.png]
Figure 2.8
Figure 2.8. Figure 2.8: Solution elements (in total 168) by solving an underdetermined system via convex optimization. [PITH_FULL_IMAGE:figures/full_fig_p061_2_8.png]
Figure 2.9
Figure 2.9. Figure 2.9: Recovery of a traffic signal via Compressed Sensing. The actual signal and its 90 random [PITH_FULL_IMAGE:figures/full_fig_p062_2_9.png]
Figure 2.10
Figure 2.10. Figure 2.10: Errors between recovered and actual traffic signals. As more samples are used in signal’s [PITH_FULL_IMAGE:figures/full_fig_p063_2_10.png]
Figure 2.11
Figure 2.11. Figure 2.11: Estimated traffic pattern of downtown San Francisco using the Cabspotting dataset ( [PITH_FULL_IMAGE:figures/full_fig_p064_2_11.png]
Figure 2.12
Figure 2.12. Figure 2.12: Correlation between every pair of days in a week. The left panel lists the normalized similarity [PITH_FULL_IMAGE:figures/full_fig_p065_2_12.png]
Figure 3.1
Figure 3.1. Figure 3.1: The systematic view of my framework. Trip records are optional as they can be inferred from [PITH_FULL_IMAGE:figures/full_fig_p072_3_1.png]
Figure 3.2
Figure 3.2. Figure 3.2: LEFT: Sample GPS points from the Cabspotting dataset. MIDDLE: Road maps of downtown San [PITH_FULL_IMAGE:figures/full_fig_p084_3_2.png]
Figure 3.3
Figure 3.3. Figure 3.3: The relationship between the normalized convergence rate and the number of iterations of the [PITH_FULL_IMAGE:figures/full_fig_p086_3_3.png]
Figure 3.4
Figure 3.4. Figure 3.4: From TOP to BOTTOM, the left diagrams show the results generated using network travel times [PITH_FULL_IMAGE:figures/full_fig_p088_3_4.png]
Figure 3.5
Figure 3.5. Figure 3.5: TOP and MIDDLE: The normalized noisy levels (%) of target road-segment flows [PITH_FULL_IMAGE:figures/full_fig_p089_3_5.png]
Figure 3.6
Figure 3.6. Figure 3.6: The error level of my technique vs. simulation-only approach: For a given road network and a [PITH_FULL_IMAGE:figures/full_fig_p091_3_6.png]
Figure 3.7
Figure 3.7. Figure 3.7: The performance speedup of my technique over the simulation-only approach: my technique is [PITH_FULL_IMAGE:figures/full_fig_p092_3_7.png]
Figure 3.8
Figure 3.8. Figure 3.8: (a) TOP: several loop-detector signals are plotted showing phase shifts among them; BOTTOM: [PITH_FULL_IMAGE:figures/full_fig_p093_3_8.png]
Figure 3.9
Figure 3.9. Figure 3.9: Qualitative (visualization) and quantitative analysis of traffic in San Francisco. Top: four time [PITH_FULL_IMAGE:figures/full_fig_p094_3_9.png]
Figure 3.10
Figure 3.10. Figure 3.10: 2D traffic animation of regions in San Francisco: Northeast (top left), Central-East (top center), [PITH_FULL_IMAGE:figures/full_fig_p095_3_10.png]
Figure 3.11
Figure 3.11. Figure 3.11: 3D traffic animation: a perspective overview (left), a topdown view (center), and a driver’s view [PITH_FULL_IMAGE:figures/full_fig_p095_3_11.png]
Figure 4.1
Figure 4.1. Figure 4.1: Collision-free trajectories generated by the expert algorithm for a vehicle traveling on the right [PITH_FULL_IMAGE:figures/full_fig_p114_4_1.png]
Figure 4.2
Figure 4.2. Figure 4.2: Illustration of important points and DANGER/SAFE labels from Section [PITH_FULL_IMAGE:figures/full_fig_p114_4_2.png]
Figure 4.3
Figure 4.3. Figure 4.3: LEFT and CENTER: the comparisons between my policy [PITH_FULL_IMAGE:figures/full_fig_p118_4_3.png]
Figure 4.4
Figure 4.4. Figure 4.4: The visualization results of collected images using t-SNE ( [PITH_FULL_IMAGE:figures/full_fig_p121_4_4.png]

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.